Systems and methods for reducing motion blur in images or video in ultra low light with array cameras

Information

  • Patent Grant
  • 10547772
  • Patent Number
    10,547,772
  • Date Filed
    Monday, October 1, 2018
    6 years ago
  • Date Issued
    Tuesday, January 28, 2020
    5 years ago
Abstract
Systems and methods for reducing motion blur in images or video in ultra low light with array cameras in accordance with embodiments of the invention are disclosed. In one embodiment, a method for synthesizing an image from multiple images captured using an array camera includes capturing image data using active cameras within an array camera, where the active cameras are configured to capture image data and the image data includes pixel brightness values that form alternate view images captured from different viewpoints, determining sets of corresponding pixels in the alternate view images where each pixel in a set of corresponding pixels is chosen from a different alternate view image, summing the pixel brightness values for corresponding pixels to create pixel brightness sums for pixel locations in an output image, and synthesizing an output image from the viewpoint of the output image using the pixel brightness sums.
Description
FIELD OF THE INVENTION

The present invention relates generally to capturing digital images and video and more specifically to the use of array cameras to reduce motion blur and/or noise when capturing images and video in low light conditions.


BACKGROUND OF THE INVENTION

Low light image capture traditionally presents challenges in producing images without excessive blurring or noise. Settings on a digital camera can typically be adjusted to compensate for low light conditions. In a digital camera, individual image sensors corresponding to pixels in an output image receive light over a predetermined exposure time (also called integration time). The exposure setting of an image sensor is typically the duration of time which light is sampled by individual pixel(s) in the image sensor. An analog gain is typically implemented through a circuit that amplifies the analog signal from a sensor before it is converted to a digital signal and processed. The exposure and gain settings on image sensors in the camera are particularly relevant in low light conditions, as increases in exposure and gain generally increase the voltage level of a pixel and thereby its apparent brightness. Under low light conditions the use of a longer exposure time can provide a brighter image but may result in motion blur, where moving objects in the scene are blurred because of movement over the time that light associated with those objects is being received by the camera. Increasing the gain can also provide a brighter image but can result in amplified noise artifacts.


SUMMARY OF THE INVENTION

Systems and methods for reducing motion blur in images or video in ultra low light with array cameras in accordance with embodiments of the invention are disclosed. In one embodiment, a method for synthesizing an image from multiple images captured from different viewpoints using an array camera includes capturing image data using active cameras within an array camera, where the active cameras are configured to capture image data and the image data captured by the active cameras includes pixel brightness values that form alternate view images captured from different viewpoints, determining sets of corresponding pixels in the alternate view images where each pixel in a set of corresponding pixels is chosen from a different alternate view image, summing the pixel brightness values for corresponding pixels in the alternate view images to create pixel brightness sums for pixel locations in an output image, and synthesizing an output image from the viewpoint of the output image using the pixel brightness sums for the pixel locations in the output image.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a conceptual illustration of an array camera architecture that can be used in a variety of array camera configurations in accordance with embodiments of the invention.



FIG. 1A conceptually illustrates an optic array and an imager array in an array camera module in accordance with an embodiment of the invention.



FIG. 2 is a conceptual illustration of a π filter arrangement that can be used in a variety of array camera configurations in accordance with embodiments of the invention.



FIG. 3 is a flow chart showing a process for summing pixel brightness values from multiple images obtained using an array camera in accordance with an embodiment of the invention.



FIG. 4 is a flow chart showing a process for summed pixel brightness values from image data captured from a reference viewpoint and one or more alternate viewpoints in accordance with embodiments of the invention.





DETAILED DISCLOSURE OF THE INVENTION

Turning now to the drawings, systems and methods for reducing motion blur in images or video in ultra low light with array cameras in accordance with embodiments of the invention are illustrated. Array cameras including camera modules that can be utilized to capture image data from different viewpoints are disclosed in U.S. patent application Ser. No. 12/935,504, entitled “Capturing and Processing of Images using Monolithic Camera Array with Heteregeneous Images”, filed May 20, 2009, the disclosure of which is incorporated by reference herein in its entirety. Array cameras offer a number of advantages and features over legacy cameras. An array camera typically contains two or more imagers (which can be referred to as cameras), each of which receives light through a separate lens system. The imagers operate to capture image data of a scene from slightly different viewpoints. Array cameras have a variety of applications, including capturing image data from multiple viewpoints that can be used in super-resolution processing and depth calculation. Imagers in the array may sense different wavelengths of light (e.g., red, green, blue, Infrared) with the application of selective filters, which can improve performance under different lighting conditions and the performance of image processing processes performed on image data captured using the array.


Array cameras in accordance with many embodiments of the invention improve the quality of images captured in low light conditions by summing the brightness of corresponding pixels from different cameras. Low light image capture is particularly challenging because the exposure time needed for a camera to receive a sufficient amount of light to produce an image can result in motion blur. Alternatively, if gain is increased to raise brightness levels, the noise level may be increased commensurately. In various embodiments of the invention, image data from a subset of cameras (imagers) in an array camera are chosen and pixel brightness values of corresponding pixels in the image data are summed, producing higher brightness levels. By summing pixel brightness values from a pixel in a reference image with a corresponding pixel from one or more alternate view image(s), the effective exposure time of the pixel in the reference image is increased by a factor equal to the number of summed pixels. Increasing exposure time can enable a reduction in analog gain and the associated noise. By exposing multiple pixels in parallel, the capture time can be significantly shorter than the effective exposure time of the pixel brightness values obtained in the reference viewpoint by summing corresponding pixels. The opportunity for motion artifacts to be present in captured image data increases with increased exposure time. Therefore, enabling an effective exposure time that is significantly longer than the actual exposure time of the pixels in the reference camera will decrease the likelihood that motion artifacts will appear in the captured image data. In addition, providing an increased effective exposure time relative to the actual capture time can provide improved low light video capture performance, where exposure time is constrained by the frame rate of the video.


In a number of embodiments, image data captured by active cameras in an array camera module is rectified (i.e. scene independent geometric shifts are applied to the image data captured by the cameras) and the rectified image data is summed. In several embodiments, parallax detection and protection processes are performed to identify scene dependent geometric corrections to apply to the image data. Systems and methods for performing parallax detection and correction are disclosed in U.S. Provisional Patent Application No. 61/691,666 entitled “Systems and Methods for Parallax Detection and Correction in Imaged Captured Using Array Cameras” to Venkataraman et al. and U.S. Pat. No. 8,619,082 entitled “Systems and Methods for Parallax Detection and Correction in Images Captured Using Array Cameras that Contain Occlusions using Subsets of Images to Perform Depth Estimation” to Ciurea et al., the disclosures of which are incorporated by reference herein in their entirety. The pixel brightness values in the image data can be summed following application of the scene dependent geometric corrections. Array cameras and methods for capturing images in low lighting conditions in accordance with embodiments of the invention are discussed further below.


Array Camera Architecture


An array camera architecture that can be used in a variety of array camera configurations in accordance with embodiments of the invention is illustrated in FIG. 1. The array camera 100 includes an imager array 102, which is connected to a processor 104. Imagers 106 in the array 102 are evenly spaced in a 5×5 square. In other embodiments, imagers may have different spacing or can be arranged in other orientations in the array. The processor 104 is hardware, software, firmware, or a combination thereof that controls various operating parameters of the imager array 102. The processor 104 can also function to process the images received from imager array 102 to produce a synthesized higher resolution image using super resolution processes, or transfer the images to other hardware, software, firmware or a combination thereof to process the images. The system can also include memory 108 in communication with the processor 104 for storing images. Architectures for imager arrays that can be utilized in accordance with embodiments of the invention include those disclosed in U.S. patent application Ser. No. 13/106,797, entitled “Architectures for System on Chip Array Cameras” to Pain et al., the disclosure of which is incorporated herein by reference in its entirety.


Although a specific architecture is illustrated in FIG. 1, any of a variety of architectures that enable the capture of low resolution images and application of super resolution processes to produce a synthesized high resolution image can be utilized in accordance with embodiments of the invention.


Array Camera Modules


Camera modules in accordance with many embodiments of the invention can be constructed from an imager array and an optic array. A camera module in accordance with an embodiment of the invention is illustrated in FIG. 1A. The camera module 200 includes an imager array 230 including an array of focal planes 240 along with a corresponding optic array 210 including an array of lens stacks 220. Within the array of lens stacks, each lens stack 220 creates an optical channel that forms an image of the scene on an array of light sensitive pixels within a corresponding focal plane 240. Each pairing of a lens stack 220 and focal plane 240 forms a single camera 104 within the camera module. Each pixel within a focal plane 240 of a camera 104 generates image data that can be sent from the camera 104 to the processor 108. In many embodiments, the lens stack within each optical channel is configured so that pixels of each focal plane 240 sample the same object space or region within the scene. In several embodiments, the lens stacks are configured so that the pixels that sample the same object space do so with sub-pixel offsets to provide sampling diversity that can be utilized to recover increased resolution through the use of super-resolution processes.


In several embodiments, color filters in individual cameras can be used to pattern the camera module with π filter groups as further discussed in U.S. Provisional Patent Application No. 61/641,165 entitled “Camera Modules Patterned with pi Filter Groups”, to Nisenzon et al. filed May 1, 2012, the disclosure of which is incorporated by reference herein in its entirety. These cameras can be used to capture data with respect to different colors, or a specific portion of the spectrum. In contrast to applying color filters to the pixels of the camera, color filters in many embodiments of the invention are included in the lens stack. For example, a green color camera can include a lens stack with a green light filter that allows green light to pass through the optical channel. In many embodiments, the pixels in each focal plane are the same and the light information captured by the pixels is differentiated by the color filters in the corresponding lens stack for each filter plane. Although a specific construction of a camera module with an optic array including color filters in the lens stacks is described above, camera modules including π filter groups can be implemented in a variety of ways including (but not limited to) by applying color filters to the pixels of the focal planes of the camera module similar to the manner in which color filters are applied to the pixels of a conventional color camera. In several embodiments, at least one of the cameras in the camera module can include uniform color filters applied to the pixels in its focal plane. In many embodiments, a Bayer filter pattern is applied to the pixels of one of the cameras in a camera module. In a number of embodiments, camera modules are constructed in which color filters are utilized in both the lens stacks and on the pixels of the imager array.


In several embodiments, an array camera generates image data from multiple focal planes and uses a processor to synthesize one or more images of a scene. In certain embodiments, the image data captured by a single focal plane in the sensor array can constitute a low resolution image (the term low resolution here is used only to contrast with higher resolution images), which the processor can use in combination with other low resolution image data captured by the camera module to construct a higher resolution image through super-resolution processing.


A 4×4 array camera module including active cameras that capture image data used to synthesize an image from the viewpoint of a reference camera in accordance with embodiments of the invention is illustrated in FIG. 2. The 4×4 camera module 250 includes an arrangement of cameras with color filters such that 3×3 subsets of cameras are patterned using π filter groups. In the illustrated embodiment, a first π filter group includes a green camera at each corner, a green reference camera in the center indicated by a box 252, blue cameras above and below the reference camera, and red cameras to the left and right sides of the reference camera. In several embodiments, the locations of the red and blue cameras within the π filter group are swapped and/or an alternative collection of cameras can be utilized to capture image data to synthesize images. In various embodiments, a second subset of active cameras includes a row of blue, green, and red cameras placed below the π filter group and a column of blue, green, and red cameras placed to the right side of the π filter group with a green camera connecting the row and the column. Although all of the cameras in the array camera module illustrated in FIG. 2 are shown as capturing image data, in many embodiments one or more of the cameras within the array camera module can be idle during image capture to conserve power as appropriate to the requirements of a specific application.


In many embodiments of the invention, one camera is designated as a reference camera capturing image data from a reference viewpoint and a number of cameras with the same color filter as the reference camera are designated alternate view cameras (that capture image data from slightly different viewpoints). In the embodiment illustrated in FIG. 2, a green camera 252 is chosen as reference camera and a plurality of other green cameras in the array including second and third green cameras 254 and 256 are chosen as alternate view cameras. The alternate view cameras can include all other green cameras or a subset of the other green cameras in the array. As will be discussed in greater detail further below, the brightness values of pixels in the alternate view cameras can be summed with the brightness values of pixels in the reference camera to increase the effective exposure time of the pixels relative to the image data capture time.


In various embodiments of the invention, an array can include multiple reference cameras with multiple subsets of alternate view cameras that can be used to synthesize images with summed pixel brightness values from different viewpoints. The diversity of image data from different viewpoints can be used in various applications such as synthesizing higher resolution images with super-resolution processes. In several embodiments of the invention, a green camera indicated by a box 258 is also a reference camera. Image data from alternate view green cameras (that can be different from the alternate view cameras associated with camera 252) are combined with image data from green camera 258 to synthesize image data representative of an image from the viewpoint of the green camera 258. The image data generated from the viewpoint of the green camera 258 can be used in combination with the image data generated from the viewpoint of the green camera 252 in applications that utilize multiple images from different viewpoints such as super-resolution processes.


Although specific array camera module configurations and partitions of cameras into subsets for synthesizing images are discussed above with respect to FIG. 2, partitions of active cameras into subsets for the purpose of capturing image data for synthesizing images can be utilized with any of a variety of camera module configurations such as, but not limited to, array camera modules including any number of cameras, array camera modules in which one or more of the cameras capture image data within multiple color channels (i.e. individual cameras in the array are possibly non-monochromatic), and/or array camera modules in which not all cameras of the camera array are active (i.e. not capturing image data or performing measurements) as appropriate to the requirements of a specific application in accordance with embodiments of the invention. Processes for utilizing array camera architectures with color filters for reducing motion blur in low light conditions is discussed below.


Reducing Motion Blur by Summing Pixel Brightness Values


As explained further above, traditional techniques to increase signal levels when capturing a digital image using image sensors include changing the exposure or gain, with some negative effects in turn. When using an array camera to capture multiple images of the same scene in the same moment an additional factor is available. By adding pixel values/signal levels together between corresponding pixels in the captured image data, a higher “boosted” signal level can be achieved with less of the negative effects of modifying the exposure and/or gain of the cameras. Furthermore, this high signal level can be achieved while lowering exposure and/or gain to decrease their negative effects.


In many embodiments of the invention, a subset of cameras in the array is chosen. Pixel values in a number of images captured by the selected cameras are combined, resulting in a pixel brightness sum corresponding to an effective exposure time equal to the number of summed pixels multiplied by the actual image data capture time. In further embodiments of the invention, because the pixel values are increased by the number of cameras from which pixel values are summed, the gain and/or exposure time of each of the cameras can be reduced accordingly.


A process for generating an image from summed pixel values from multiple images captured by an array camera in accordance with embodiments of the invention is illustrated in FIG. 3. The process 300 includes determining (310) image capture settings for active cameras in an array. Image capture settings can include (but are not limited to) exposure time, analog gain, and frame rate (when capturing video). As will be discussed further below, these settings can be adjusted based on the number of active cameras used.


Image data is captured (320) using a set of active cameras in the array. Typically, each camera produces an image from its point of view and the image data forms images made up of pixel brightness values. In array cameras, often one camera is designated a reference camera and the image data captured by that camera is referred to as being captured from a reference viewpoint. In several embodiments of the invention, the set of active cameras is chosen where the cameras have color filters such that they capture information within the same spectral band (also referred to as color channel). A spectral band can correspond to a human perceptible color (e.g., red, green, blue) or can be non-perceptible (e.g., infrared). As discussed further above, an array camera may utilize a filter pattern such that it contains cameras that separately capture green, red, and blue light (designated green, red, and blue cameras respectively). Therefore, a set of active cameras can include all cameras of one color, such as green cameras.


The pixel brightness values of corresponding pixels in the image data captured from alternate viewpoints are summed (340). Corresponding pixels can refer to pixels that represent the same scene content in each image. Parallax, due to the different fields of view of the active cameras, can affect pixel correspondence. Some pixels that capture the same portion of a scene (i.e. pixel brightness values corresponding to samples of corresponding portions of the object space) may be in different locations in different images due to parallax. In many embodiments of the invention, images are compensated (330) for parallax. Parallax compensation such as applying scene-dependent geometric shifts to the affected pixels is discussed further below. Images can also be compensated for distortions due to the physical characteristics of the particular imager (also referred to as geometric compensation or correction) using scene-independent geometric shifts to align corresponding pixels.


In many embodiments of the invention, one image is designated a reference image and the other images in the set of images are referred to as alternate view images. The designation of a reference image has particular relevance in determining a reference viewpoint and compensating for parallax. Where one image is designated a reference image, the pixels of the reference image are summed with corresponding pixels in alternate view images. In other embodiments, the reference image may be a ‘virtual’ image synthesized in a location in the array where no physical camera exists. When a ‘virtual’ image is used, the corresponding pixels from alternate view images can be summed into the grid for a reference viewpoint where image data may not physically exist. Systems and methods for generating a ‘virtual’ image from the perspective of a given viewpoint include, but are not limited to, those disclosed in U.S. Provisional Application No. 61/707,691 entitled “Synthesizing Images From Light Fields Utilizing Virtual Viewpoints” to Jain, filed Sep. 28, 2012, U.S. application Ser. No. 14/042,275 entitled “Generating Images from Light Fields Utilizing Virtual Viewpoints” to Nisenzon et al., filed Sep. 30, 2013, U.S. Provisional Application No. 61/776,751 entitled “Systems and Methods for Image Data Compression” to McMahon et al., filed Mar. 11, 2013, and U.S. application Ser. No. 14/204,990 entitled “Systems and Methods for Image Data Compression” to McMahon et al., filed Mar. 11, 2014, the disclosures of which are hereby incorporated by reference in their entirety.


An output image is synthesized (350) from the pixel brightness sums. Where a reference image has been designated, values equal to the pixel brightness sums corresponding to pixels in the reference image can be placed in the same locations in the output image. Where multiple references images have been designated, super-resolution processes can be utilized to synthesize a higher resolution image using the pixel brightness sums determined for each of the reference images. In other words, multiple independently summed images can be used as inputs to a super-resolution process to generate a higher resolution image.


In further embodiments of the invention, image capture settings such as exposure time and gain can be adjusted in view of the higher signal levels resulting from the summation. For example, presume image data is captured using a 4×4 array where eight cameras have green filters. Image data can be combined by summing the corresponding pixels from the eight green cameras. Given that that there are eight active green cameras, the pixel brightness values are approximately eight times higher than the pixel brightness values of an image from a single green camera. Under these conditions, the exposure time can be reduced by eight times, having the effect of maintaining the same apparent brightness and noise while reducing the apparent motion blur. Alternatively, the analog gain of cameras can be reduced by eight times, having the effect of reducing the apparent noise in the image while maintaining the same brightness, or the exposure time and the analog gain can each be reduced by an amount (such as half exposure and one quarter analog gain) so that there is a total reduction by a factor of eight.


If multiple images are captured over time to generate a video sequence, the frame rate (i.e., rate at which images are captured) can be adjusted. The theoretical maximum exposure time is the inverse of frame rate. As exposure time is decreased, the frame rate can be increased accordingly. Image capture settings such as exposure time, gain, and frame rate can be determined (310) before the images are captured. Although specific processes for increasing the effective exposure times of pixels in a reference image relative to the actual image data capture time by a factor equal to the number of summed pixels are discussed above with reference to FIG. 3, any of a variety of processes can be utilized to increase effective exposure times of pixels in a reference image by summing the pixel brightness values in the reference image with the pixel brightness values of corresponding pixels in alternate view images can be utilized as appropriate to the requirements of specific applications in accordance with embodiments of the invention. The effects of parallax and techniques to compensate for parallax when summing corresponding pixels to increase effective pixel exposure times in accordance with embodiments of the invention are discussed below.


Disparity and Compensating for Parallax


Images of a scene captured by different cameras in an array camera can have slight differences due to the different fields of view resulting from the different locations of the cameras, an effect known as parallax. These differences, referred to as disparity, provide information that can be used to measure depth of objects within a scene. Once depth information has been determined, scene-dependent geometric shifts can be applied to the pixels of the captured images to remove the differences in the images that resulted from parallax. The modified images then have similar pixels, corresponding to the same observed points in the scene, in the same locations. Systems and methods for detecting and correcting parallax are discussed in U.S. Patent Application Ser. No. 61/691,666 entitled “Systems and Methods for Parallax Detection and Correction in Images Captured Using Array Cameras” to Venkataraman et al. and U.S. Pat. No. 8,619,082, the disclosures of which are incorporated by reference above.


Techniques such as those disclosed in the patent application incorporated above are typically used to generate a depth map from a reference viewpoint. The reference viewpoint can be from the viewpoint of one of the cameras in a camera array. Alternatively, the reference viewpoint can be an arbitrary virtual viewpoint. A depth map indicates the distance of the surfaces of scene objects from a reference viewpoint and can be utilized to determine scene dependent geometric corrections to apply to the pixels from each of the images within captured image data to eliminate disparity when fusing images together as in super-resolution processing (generating a higher-resolution image from multiple lower-resolution images) and/or when summing pixel brightness values for corresponding pixels.


Corrections for parallax may be desired when parallax results in scene differences in the images used for corresponding pixel summation. In several embodiments of the invention, the processes discussed above can be utilized without parallax correction where no objects are within a certain distance or where the minimum depth at which objects appear in the image (or equivalently, disparity) is determined to be below a certain threshold. For example, sparse depth information can be generated for a reference image and the pixel brightness values in the reference view image data are summed with pixel brightness values in alternate view image data when no objects are within a threshold distance according to the generated depth information. In further embodiments of the invention, images can be compensated for parallax in all situations or where the depth of objects in the image is determined to be above a predetermined threshold.


A process for generating summed pixel values from image data captured from a reference viewpoint and one or more alternate viewpoints, where parallax detection and correction is utilized to identify corresponding pixels within the image data in the in accordance with embodiments of the invention is illustrated in FIG. 4. Similar to the process described above with respect to FIG. 3, image capture settings can be determined (410) and image data captured (420). Depth measurements are calculated using at least a portion of the image data (430) and a determination is made whether any objects in the scene are within a predetermined threshold depth/distance from the camera (440). If no objects are within the predetermined threshold, pixel brightness values are summed (450) and an output image generated (460). If there is at least one object within the threshold distance, parallax correction is performed. Techniques for correcting for parallax can be utilized in a variety of ways. Processes such as those disclosed in U.S. Provisional Patent Application No. 61/780,974 entitled “Systems and Methods for Synthesizing Images from Image Data Captured by an Array Camera Using Depth Maps in which Depth Estimation Precision and Spatial Resolution Vary” to Venkataraman et al. and U.S. patent application Ser. No. 14/207,254 entitled “Systems and Methods for Synthesizing Images from Image Data Captured by an Array Camera Using Restricted Depth of Field Depth Maps in which Depth Estimation Precision Varies” to Venkataraman et al. can be utilized to correct parallax in images before pixel summing as discussed below. The disclosures of U.S. Patent Application Ser. No. 61/780,974 and Ser. No. 14/207,254 are hereby incorporated by reference in their entirety. In many embodiments of the invention, parallax detection and compensation includes one or more modes as will be discussed below.


In a first mode of operation, referred to here as user plane focus mode, parallax detection and correction is only performed with respect to pixel brightness values that sample objects in the scene that are at a specific focus depth and/or within a specific depth of field relative to the focus depth. The focus depth can be determined from at least one designated region of interest within the captured image and/or a preview image. The region of interest can be a predetermined location (e.g., an auto-focus rectangle in the center of the image) or can be selected by a user in real time. A sparse depth map can be created with higher resolution with respect to regions of the image containing objects that are located within the specified depth of field relative to the specified focus depth. Systems and methods for generating sparse depth maps include, but are not limited to, those disclosed in U.S. Patent Application Ser. No. 61/780,974 and Ser. No. 14/207,254 incorporated by reference above. In some embodiments, the depth detection might occur with denser depth sampling in the desired depth-of-field and less dense depth sampling outside the desired depth-of-field. In other embodiments, the depth of field may be set to be extremely small such that the depth detection might be entirely skipped and the depth map may be assumed to consist only of pixels at the target depth. Using the depth map, the disparity between a reference viewpoint and the alternate viewpoints can be determined (470) using the baseline between each of the alternate view cameras and the reference camera. The disparity can then be used to identify (480) corresponding pixels within the image data based upon the anticipated scene-dependent geometric shifts present in the image data. In this way, the pixels in the reference plane that are in focus are summed using corresponding pixels identified in a manner that accounts for disparity between the reference viewpoint and an alternate viewpoint. In this way, the parallax detection and correction process can reduce any blur that may be introduced by summing pixels that are incorrectly identified as corresponding. After parallax compensation, objects in the target focus depth of field will be aligned across the alternate images and summing the pixels containing those objects provides a higher effective exposure in the final image for those objects. Pixels containing objects not at the target focus depth may not be aligned across the images and summing those pixels may result in blurring. However, there may already be blurring due to the target focus not being set for those objects (they are “out of focus”) so the additional blurring may not be visually significant.


In a second mode, referred to here as dense parallax compensated mode, parallax correction is performed for all pixels in an image before being summed. In this mode, a depth map is calculated for all pixels in a reference image and corresponding pixels in the alternate images are identified based upon the scene-dependent geometric shifts (470) (480) with respect to the reference image predicted by the depth map.


In a third mode, all pixels in an image are compensated before being summed. However, the same compensation is applied to all pixels irrespective of their depth indicated on a depth map. The uniform compensation can be based on a chosen depth. In many embodiments, the depth can be determined by identifying a region of interest (using techniques such as those discussed above) and calculating a depth map of the region. In several embodiments, a histogram of the depths in the region is formed. In some embodiments, the histogram can be filtered to eliminate low confidence regions of the depth map such as textureless regions or to admit only high confidence regions such as edges. In many embodiments, the median depth of the histogram is taken to be the desired depth of focus. Systems and methods for determining a depth of focus include, but are not limited to, those disclosed in U.S. Patent Application Ser. No. 61/780,974 and Ser. No. 14/207,254 incorporated by reference above. The depth is then used to compensate all pixels before summing (can be seen as equivalent to setting a depth map to a fixed depth everywhere). This will tend to attenuate regions of the image that are off the desired focal depth (not aligned) and amplify regions which are on or close to the focal depth (aligned). For objects in the reference viewpoint which are actually at or near the target depth, corresponding pixels will naturally align and the summed image will have an appearance of sharpness. For objects in the reference viewpoint which are not actually at or near the target depth, the resulting summed pixel will be an average of many non-corresponding pixels. The resulting image will typically be a synthetic aperture image where objects at the target depth will appear sharper and brighter than objects far from the target depth, which will appear blurred. This mode can allow reduced computation in situations where parallax processing is too computationally demanding, or in applications where the scene content is typically at a fixed depth (such as usually beyond a certain distance). In many embodiments, once the final image is summed it is divided or multiplied by a scale factor as needed to set a desired target brightness for final output.


When image data is captured from different perspectives and the scene includes foreground objects, the disparity in the location of the foreground object in each of the images results in portions of the scene behind the foreground object being visible in some but not all of the images. A pixel that captures image data concerning a portion of a scene, which is not visible in images captured of the scene from other viewpoints, can be referred to as an occluded pixel. These occlusions can be detected by determining whether there is a great difference between pixels that should correspond according to depth map and disparity calculations. In many embodiments, at least some portions of images that have these occlusions are not included in the summation to avoid creating artifacts. Systems and methods for detecting occlusions and correcting for parallax include, but are not limited to, those described in U.S. Patent Application Ser. No. 61/691,666 and U.S. Pat. No. 8,619,082 incorporated by reference above.


In several embodiments, where occluded pixels are detected, only those pixels corresponding to pixels visible in the reference image can be used in the summation. In many embodiments, the occluded pixels can be left out of the summation. If pixels are left out of the summation, the total of the pixels that are summed should be scaled by a factor to match the brightness of the other summed pixels. The factor can be determined by dividing the total number of images that could potentially include the pixel by the number of cameras that actually observe that pixel. For example assume Ng cameras are summed for areas of an image where all pixels are visible and Ngv cameras have visibility of certain pixels in an occluded area. Those pixels with visibility are summed and the sum is multiplied by a factor of Ng/Ngv to compensate for the pixels left out.


Although specific techniques for parallax compensation are discussed above with respect to processes for summing pixel brightness values, any of a variety of processes can be utilized to correct parallax in accordance with embodiments of the invention. Temporal frame compensation in accordance with embodiments of the invention is discussed below.


Motion-Compensated Temporal Filtering


Noise and signal performance of array cameras in low light conditions can be further improved by temporal frame compensation. Multiple frames are captured of a scene over time (e.g., frames captured at times N−1, N, and N+1). For each pixel a motion compensation vector is determined between each frame. The motion compensation vector can be calculated between individual camera images captured for each frame or between summed images formed from the images corresponding to each frame (that is, summed image at N−1, summed image at N, and image at N+1). Using the motion compensation vectors, the brightness values of corresponding pixels between frames are added to generate an image representing the sum of images from multiple cameras and multiple frames. This summation can be divided to produce an average (which may tend to reduce noise) or can be compensated for by reducing exposure time (which may tend to reduce motion blur). Producing a color image from image data captured within discrete narrow spectral bands in accordance with embodiments of the invention is discussed below.


Combining Image Data Captured in Narrow Spectral Bands into a Color Image


When utilized with sets of cameras that capture a single color, the techniques discussed above generally produce a monochrome image in that color. In many embodiments of the invention, a composite color RGB image can be formed from monochrome images of different colors (i.e., separate color channels). As discussed above, a monochrome image can be formed by summing pixel brightness values of images from a set of cameras with the same color filter in an array. The array can contain sets of green cameras, red cameras, and blue cameras. Recognizing that the number of cameras in each set may not be equal, the summed pixel brightness values can be normalized across the sets of cameras and the summed images can be combined into a color image.


Assuming the set of green cameras has the largest number of cameras, the images from the sets of red and blue cameras can be normalized as follows. A green image is generated using the processes described above to sum pixel brightness values from green cameras. Similarly, a red image is generated from the red cameras and a blue image is generated from the blue cameras. The pixel brightness values of the red image are increased by a factor Ng/Nr where Ng is the number of green cameras and Nr is the number of red cameras. Similarly, the pixel brightness values of the blue image are increased by a factor Ng/Nb where Nb is the number of blue cameras. The images can then be combined into a color image where the relative color levels are correct with respect to each other. Increasing the signal levels for normalization can be accomplished in a variety of ways including increasing the analog gain of cameras or multiplying the signal in the digital domain after analog-to-digital conversion.


Although the present invention has been described in certain specific aspects, many additional modifications and variations would be apparent to those skilled in the art. It is therefore to be understood that the present invention may be practiced otherwise than specifically described, including various changes in the implementation such as utilizing encoders and decoders that support features beyond those specified within a particular standard with which they comply, without departing from the scope and spirit of the present invention. Thus, embodiments of the present invention should be considered in all respects as illustrative and not restrictive.

Claims
  • 1. A method for synthesizing an image from multiple images captured from different viewpoints using an array camera, the method comprising: capturing image data using a plurality of active cameras within an array camera, where the plurality of active cameras are configured to capture image data comprising pixel brightness values that form a reference image and a plurality of alternate view images captured from different viewpoints;applying geometric corrections to find correspondences between pixels in the plurality of alternate view images and pixels in the reference image using a processor configured by software using depth information;summing the pixel brightness values for pixels in the reference image with pixel brightness values for corresponding pixels in the alternate view images to create pixel brightness sums for the pixel locations in the reference image using the processor configured by software; andsynthesizing an output image from the viewpoint of the reference image using image data comprising the pixel brightness sums for the pixel locations in the reference image using the processor configured by software.
  • 2. The method of claim 1, further comprising: performing parallax detection using the processor configured by software to identify scene dependent geometric shifts to apply to the alternate images by comparing the reference image and the alternate images;wherein applying geometric corrections to find correspondences between pixels in the plurality of alternate images and pixels in the reference image further comprises applying scene dependent geometric shifts to the plurality of alternate images to compensate for parallax.
  • 3. The method of claim 2, further comprising: identifying pixels in the alternate images that are occluded in the reference image using the processor configured by software; andleaving occluded pixels out when summing the pixel brightness values for pixels in the reference image with pixel brightness values for corresponding pixels in the alternate images using the processor configured by software.
  • 4. The method of claim 1, further comprising: performing parallax detection using the processor configured by software to identify scene dependent geometric shifts to apply to at least a portion of the pixels in the alternative images by comparing the reference image and the alternative images; andwhen parallax detection identifies at least one pixel within a threshold distance of the reference viewpoint, applying scene dependent geometric shifts to the plurality of alternative images to compensate for parallax.
  • 5. The method of claim 1, further comprising: performing parallax detection using the processor configured by software to identify scene dependent geometric shifts to apply to at least a portion of the pixels in the alternative images by comparing the reference image and the alternative images; andwhen parallax detection determines that a pixel from the reference viewpoint has a depth within a specified depth of field, applying scene dependent geometric shifts to corresponding pixels in the alternative images to compensate for parallax.
  • 6. The method of claim 1, wherein applying geometric corrections to find correspondences between pixels in the plurality of alternate images and pixels in the reference image further comprises applying a fixed parallax shift to pixels in each of the plurality of alternative images.
  • 7. The method of claim 1, further comprising: capturing a second set of image data using the plurality of active cameras and synthesizing a second output image using the processor configured by software;calculating motion compensation vectors for the second output image using the processor configured by software;applying motion compensation shifts to shift the second output image to the viewpoint of the output image using the processor configured by software;summing the pixel brightness values for pixels in the output image with pixel brightness values for corresponding pixels in the second output image to create pixel brightness sums for the pixel locations in the output image using the processor configured by software; andsynthesizing a motion compensated output image from the viewpoint of the reference image using the pixel brightness sums for the pixel locations in the output image using the processor configured by software.
  • 8. The method of claim 1, wherein the plurality of active cameras that capture the reference image and the alternative images form a first subset of cameras and the method further comprises: capturing image data using a second subset of active cameras within the array camera, where the second subset of active cameras are configured to capture image data comprising pixel brightness values that form a second reference image and a second set of alternative images captured from different viewpoints;applying geometric shifts to shift the second set of alternative images to the viewpoint of the second reference image using the processor configured by software;summing the pixel brightness values for pixels in the second reference image with pixel brightness values for corresponding pixels in the second set of alternative images to create pixel brightness sums for the pixel locations in the second reference image using a processor configured by software;synthesizing an alternate view output image from the viewpoint of the second reference image using the pixel brightness sums for the pixel locations in the second reference image using the processor configured by software; andsynthesizing a high resolution image using the processor configured by software to perform a super resolution process based upon the output image and the alternate view output image.
  • 9. The method of claim 1, wherein the array camera comprises cameras that capture image data within different spectral bands.
  • 10. The method of claim 1, wherein the reference image is a virtual image synthesized in a location where none of the active cameras exist.
  • 11. An imaging device for synthesizing an image from multiple images captured from different viewpoints, comprising: an array camera;memory containing an image synthesizer application for causing a processor to perform the steps of:capturing image data using a plurality of active cameras within the array camera, where the plurality of active cameras are configured to capture image data comprising pixel brightness values that form a reference image and a plurality of alternate view images captured from different viewpoints;applying geometric corrections to find correspondences between pixels in the plurality of alternate view images and pixels in the reference image using the processor configured by software using depth information;summing the pixel brightness values for pixels in the reference image with pixel brightness values for corresponding pixels in the alternate view images to create pixel brightness sums for the pixel locations in the reference image using the processor configured by software; andsynthesizing an output image from the viewpoint of the reference image using image data comprising the pixel brightness sums for the pixel locations in the reference image using the processor configured by software.
  • 12. The imaging device of claim 11, wherein the steps further comprise: performing parallax detection using the processor configured by software to identify scene dependent geometric shifts to apply to the alternate images by comparing the reference image and the alternate images;wherein applying geometric corrections to find correspondences between pixels in the plurality of alternate images and pixels in the reference image further comprises applying scene dependent geometric shifts to the plurality of alternate images to compensate for parallax.
  • 13. The imaging device of claim 12, wherein the steps further comprise: identifying pixels in the alternate images that are occluded in the reference image using the processor configured by software; andleaving occluded pixels out when summing the pixel brightness values for pixels in the reference image with pixel brightness values for corresponding pixels in the alternate images using the processor configured by software.
  • 14. The imaging device of claim 11, wherein the steps further comprise: performing parallax detection using the processor configured by software to identify scene dependent geometric shifts to apply to at least a portion of the pixels in the alternative images by comparing the reference image and the alternative images; andwhen parallax detection identifies at least one pixel within a threshold distance of the reference viewpoint, applying scene dependent geometric shifts to the plurality of alternative images to compensate for parallax.
  • 15. The imaging device of claim 11, wherein the steps further comprise: performing parallax detection using the processor configured by software to identify scene dependent geometric shifts to apply to at least a portion of the pixels in the alternative images by comparing the reference image and the alternative images; andwhen parallax detection determines that a pixel from the reference viewpoint has a depth within a specified depth of field, applying scene dependent geometric shifts to corresponding pixels in the alternative images to compensate for parallax.
  • 16. The imaging device of claim 11, wherein applying geometric corrections to find correspondences between pixels in the plurality of alternate images and pixels in the reference image further comprises applying a fixed parallax shift to pixels in each of the plurality of alternative images.
  • 17. The imaging device of claim 11, wherein the steps further comprise: capturing a second set of image data using the plurality of active cameras and synthesizing a second output image using the processor configured by software;calculating motion compensation vectors for the second output image using the processor configured by software;applying motion compensation shifts to shift the second output image to the viewpoint of the output image using the processor configured by software;summing the pixel brightness values for pixels in the output image with pixel brightness values for corresponding pixels in the second output image to create pixel brightness sums for the pixel locations in the output image using the processor configured by software; andsynthesizing a motion compensated output image from the viewpoint of the reference image using the pixel brightness sums for the pixel locations in the output image using the processor configured by software.
  • 18. The imaging device of claim 11, wherein the plurality of active cameras that capture the reference image and the alternative images form a first subset of cameras and the method further comprises: capturing image data using a second subset of active cameras within the array camera, where the second subset of active cameras are configured to capture image data comprising pixel brightness values that form a second reference image and a second set of alternative images captured from different viewpoints;applying geometric shifts to shift the second set of alternative images to the viewpoint of the second reference image using the processor configured by software;summing the pixel brightness values for pixels in the second reference image with pixel brightness values for corresponding pixels in the second set of alternative images to create pixel brightness sums for the pixel locations in the second reference image using a processor configured by software;synthesizing an alternate view output image from the viewpoint of the second reference image using the pixel brightness sums for the pixel locations in the second reference image using the processor configured by software; andsynthesizing a high resolution image using the processor configured by software to perform a super resolution process based upon the output image and the alternate view output image.
  • 19. The imaging device of claim 11, wherein the array camera comprises cameras that capture image data within different spectral bands.
  • 20. The imaging device of claim 11, wherein the reference image is a virtual image synthesized in a location where none of the active cameras exist.
CROSS-REFERENCE TO RELATED APPLICATIONS

The current application is a continuation of U.S. patent application Ser. No. 15/438,542 entitled “Systems and Methods for Reducing Motion Blur in Images or Video in Ultra Low Light with Array Cameras” to Gabriel Molina filed Feb. 21, 2017 which application is a continuation of U.S. patent application Ser. No. 14/776,553, entitled “Systems and Methods for Reducing Motion Blur in Images or Video in Ultra Low Light with Array Cameras” to Gabriel Molina, filed Sep. 14, 2015 and issued as U.S. Pat. No. 9,578,259 on Feb. 21, 2017, which application is a 35 U.S.C. § 371 National Stage patent application of PCT Patent Application Serial No. PCT/US2014/025100 entitled “Systems and Methods for Reducing Motion Blur in Images or Video in Ultra Low Light With Array Cameras” to Gabriel Molina, filed Mar. 12, 2014, which application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application Ser. No. 61/783,441, entitled “Systems and Methods for Reducing Motion Blur in Images or Video in Ultra Low Light with Array Cameras” to Gabriel Molina filed Mar. 13, 2013, the disclosures of which are hereby incorporated by reference in their entirety.

US Referenced Citations (1103)
Number Name Date Kind
4124798 Thompson Nov 1978 A
4198646 Alexander et al. Apr 1980 A
4323925 Abell et al. Apr 1982 A
4460449 Montalbano Jul 1984 A
4467365 Murayama et al. Aug 1984 A
4652909 Glenn Mar 1987 A
4899060 Lischke Feb 1990 A
4962425 Rea Oct 1990 A
5005083 Grage Apr 1991 A
5070414 Tsutsumi Dec 1991 A
5144448 Hornbaker et al. Sep 1992 A
5157499 Oguma et al. Oct 1992 A
5325449 Burt Jun 1994 A
5327125 Iwase et al. Jul 1994 A
5463464 Ladewski Oct 1995 A
5488674 Burt Jan 1996 A
5629524 Stettner et al. May 1997 A
5638461 Fridge Jun 1997 A
5757425 Barton et al. May 1998 A
5793900 Nourbakhsh et al. Aug 1998 A
5801919 Griencewic Sep 1998 A
5808350 Jack et al. Sep 1998 A
5832312 Rieger et al. Nov 1998 A
5833507 Woodgate et al. Nov 1998 A
5880691 Fossum et al. Mar 1999 A
5911008 Niikura et al. Jun 1999 A
5933190 Dierickx et al. Aug 1999 A
5963664 Kumar et al. Oct 1999 A
5973844 Burger Oct 1999 A
6002743 Telymonde Dec 1999 A
6005607 Uomori et al. Dec 1999 A
6034690 Gallery et al. Mar 2000 A
6069351 MacK May 2000 A
6069365 Chow et al. May 2000 A
6095989 Hay et al. Aug 2000 A
6097394 Levoy et al. Aug 2000 A
6124974 Burger Sep 2000 A
6130786 Osawa et al. Oct 2000 A
6137100 Fossum et al. Oct 2000 A
6137535 Meyers Oct 2000 A
6141048 Meyers Oct 2000 A
6160909 Melen Dec 2000 A
6163414 Kikuchi et al. Dec 2000 A
6172352 Liu et al. Jan 2001 B1
6175379 Uomori et al. Jan 2001 B1
6205241 Melen Mar 2001 B1
6239909 Hayashi et al. May 2001 B1
6292713 Jouppi et al. Sep 2001 B1
6340994 Margulis et al. Jan 2002 B1
6358862 Ireland et al. Mar 2002 B1
6373518 Sogawa Apr 2002 B1
6419638 Hay et al. Jul 2002 B1
6443579 Myers Sep 2002 B1
6445815 Sato Sep 2002 B1
6476805 Shum et al. Nov 2002 B1
6477260 Shimomura Nov 2002 B1
6502097 Chan et al. Dec 2002 B1
6525302 Dowski, Jr. et al. Feb 2003 B2
6552742 Seta Apr 2003 B1
6563537 Kawamura et al. May 2003 B1
6571466 Glenn et al. Jun 2003 B1
6603513 Berezin Aug 2003 B1
6611289 Yu Aug 2003 B1
6627896 Hashimoto et al. Sep 2003 B1
6628330 Lin Sep 2003 B1
6628845 Stone et al. Sep 2003 B1
6635941 Suda Oct 2003 B2
6639596 Shum et al. Oct 2003 B1
6647142 Beardsley Nov 2003 B1
6657218 Noda Dec 2003 B2
6671399 Berestov Dec 2003 B1
6674892 Melen Jan 2004 B1
6750904 Lambert Jun 2004 B1
6765617 Tangen et al. Jul 2004 B1
6771833 Edgar Aug 2004 B1
6774941 Boisvert et al. Aug 2004 B1
6788338 Dinev Sep 2004 B1
6795253 Shinohara Sep 2004 B2
6801653 Wu et al. Oct 2004 B1
6819328 Moriwaki et al. Nov 2004 B1
6819358 Kagle et al. Nov 2004 B1
6879735 Portniaguine et al. Apr 2005 B1
6897454 Sasaki et al. May 2005 B2
6903770 Kobayashi et al. Jun 2005 B1
6909121 Nishikawa Jun 2005 B2
6917702 Beardsley Jul 2005 B2
6927922 George Aug 2005 B2
6958862 Joseph Oct 2005 B1
6985175 Iwai et al. Jan 2006 B2
7015954 Foote et al. Mar 2006 B1
7085409 Sawhney Aug 2006 B2
7161614 Yamashita et al. Jan 2007 B1
7199348 Olsen et al. Apr 2007 B2
7206449 Raskar et al. Apr 2007 B2
7215364 Wachtel et al. May 2007 B2
7235785 Hornback et al. Jun 2007 B2
7245761 Swaminathan et al. Jul 2007 B2
7262799 Suda Aug 2007 B2
7292735 Blake et al. Nov 2007 B2
7295697 Satoh Nov 2007 B1
7333651 Kim et al. Feb 2008 B1
7369165 Bosco et al. May 2008 B2
7391572 Jacobowitz et al. Jun 2008 B2
7408725 Sato Aug 2008 B2
7425984 Chen Sep 2008 B2
7430312 Gu Sep 2008 B2
7471765 Jaffray et al. Dec 2008 B2
7496293 Shamir et al. Feb 2009 B2
7564019 Olsen Jul 2009 B2
7599547 Sun et al. Oct 2009 B2
7606484 Richards et al. Oct 2009 B1
7620265 Wolff Nov 2009 B1
7633511 Shum et al. Dec 2009 B2
7639435 Chiang et al. Dec 2009 B2
7639838 Nims Dec 2009 B2
7646549 Zalevsky et al. Jan 2010 B2
7657090 Omatsu et al. Feb 2010 B2
7667824 Moran Feb 2010 B1
7675080 Boettiger Mar 2010 B2
7675681 Tomikawa et al. Mar 2010 B2
7706634 Schmitt et al. Apr 2010 B2
7723662 Levoy et al. May 2010 B2
7738013 Galambos et al. Jun 2010 B2
7741620 Doering et al. Jun 2010 B2
7782364 Smith Aug 2010 B2
7826153 Hong Nov 2010 B2
7840067 Shen et al. Nov 2010 B2
7912673 Hébert et al. Mar 2011 B2
7924321 Nayar et al. Apr 2011 B2
7956871 Fainstain et al. Jun 2011 B2
7965314 Miller et al. Jun 2011 B1
7973834 Yang Jul 2011 B2
7986018 Rennie Jul 2011 B2
7990447 Honda et al. Aug 2011 B2
8000498 Shih et al. Aug 2011 B2
8013904 Tan et al. Sep 2011 B2
8027531 Wilburn et al. Sep 2011 B2
8044994 Vetro et al. Oct 2011 B2
8055466 Bryll Nov 2011 B2
8077245 Adamo et al. Dec 2011 B2
8089515 Chebil et al. Jan 2012 B2
8098297 Crisan et al. Jan 2012 B2
8098304 Pinto et al. Jan 2012 B2
8106949 Tan et al. Jan 2012 B2
8111910 Tanaka Feb 2012 B2
8126279 Marcellin et al. Feb 2012 B2
8130120 Kawabata et al. Mar 2012 B2
8131097 Lelescu et al. Mar 2012 B2
8149323 Li Apr 2012 B2
8164629 Zhang Apr 2012 B1
8169486 Corcoran et al. May 2012 B2
8180145 Wu et al. May 2012 B2
8189065 Georgiev et al. May 2012 B2
8189089 Georgiev et al. May 2012 B1
8194296 Compton Jun 2012 B2
8212914 Chiu Jul 2012 B2
8213711 Tam Jul 2012 B2
8231814 Duparre Jul 2012 B2
8242426 Ward et al. Aug 2012 B2
8244027 Takahashi Aug 2012 B2
8244058 Intwala et al. Aug 2012 B1
8254668 Mashitani et al. Aug 2012 B2
8279325 Pitts et al. Oct 2012 B2
8280194 Wong et al. Oct 2012 B2
8284240 Saint-Pierre et al. Oct 2012 B2
8289409 Chang Oct 2012 B2
8289440 Pitts et al. Oct 2012 B2
8290358 Georgiev Oct 2012 B1
8294099 Blackwell, Jr. Oct 2012 B2
8294754 Jung et al. Oct 2012 B2
8300085 Yang et al. Oct 2012 B2
8305456 McMahon Nov 2012 B1
8315476 Georgiev et al. Nov 2012 B1
8345144 Georgiev et al. Jan 2013 B1
8360574 Ishak et al. Jan 2013 B2
8400555 Georgiev Mar 2013 B1
8406562 Bassi et al. Mar 2013 B2
8411146 Twede Apr 2013 B2
8446492 Nakano et al. May 2013 B2
8456517 Mor et al. Jun 2013 B2
8493496 Freedman et al. Jul 2013 B2
8514291 Chang Aug 2013 B2
8514491 Duparre Aug 2013 B2
8541730 Inuiya Sep 2013 B2
8542933 Venkataraman et al. Sep 2013 B2
8553093 Wong et al. Oct 2013 B2
8559705 Ng Oct 2013 B2
8559756 Georgiev et al. Oct 2013 B2
8565547 Strandemar Oct 2013 B2
8576302 Yoshikawa Nov 2013 B2
8577183 Robinson Nov 2013 B2
8581995 Lin et al. Nov 2013 B2
8619082 Ciurea Dec 2013 B1
8648918 Kauker et al. Feb 2014 B2
8648919 Mantzel et al. Feb 2014 B2
8655052 Spooner et al. Feb 2014 B2
8682107 Yoon et al. Mar 2014 B2
8687087 Pertsel et al. Apr 2014 B2
8692893 McMahon Apr 2014 B2
8754941 Sarwari et al. Jun 2014 B1
8773536 Zhang Jul 2014 B1
8780113 Ciurea et al. Jul 2014 B1
8804255 Duparre Aug 2014 B2
8823813 Mantzel et al. Sep 2014 B2
8830375 Ludwig Sep 2014 B2
8831367 Venkataraman et al. Sep 2014 B2
8831377 Pitts et al. Sep 2014 B2
8836793 Kriesel et al. Sep 2014 B1
8842201 Tajiri Sep 2014 B2
8854462 Herbin et al. Oct 2014 B2
8861089 Duparre Oct 2014 B2
8866912 Mullis Oct 2014 B2
8866920 Venkataraman et al. Oct 2014 B2
8866951 Keelan Oct 2014 B2
8878950 Lelescu et al. Nov 2014 B2
8885059 Venkataraman et al. Nov 2014 B1
8885922 Ito et al. Nov 2014 B2
8896594 Xiong et al. Nov 2014 B2
8896719 Venkataraman et al. Nov 2014 B1
8902321 Venkataraman et al. Dec 2014 B2
8928793 McMahon Jan 2015 B2
8977038 Tian et al. Mar 2015 B2
9001226 Ng et al. Apr 2015 B1
9019426 Han Apr 2015 B2
9025894 Venkataraman May 2015 B2
9025895 Venkataraman May 2015 B2
9030528 Pesach et al. May 2015 B2
9031335 Venkataraman May 2015 B2
9031342 Venkataraman May 2015 B2
9031343 Venkataraman May 2015 B2
9036928 Venkataraman May 2015 B2
9036931 Venkataraman et al. May 2015 B2
9041823 Venkataraman et al. May 2015 B2
9041824 Lelescu et al. May 2015 B2
9041829 Venkataraman et al. May 2015 B2
9042667 Venkataraman et al. May 2015 B2
9047684 Lelescu et al. Jun 2015 B2
9049367 Venkataraman et al. Jun 2015 B2
9055233 Venkataraman et al. Jun 2015 B2
9060120 Venkataraman et al. Jun 2015 B2
9060124 Venkataraman et al. Jun 2015 B2
9077893 Venkataraman et al. Jul 2015 B2
9094661 Venkataraman et al. Jul 2015 B2
9100586 McMahon et al. Aug 2015 B2
9100635 Duparre et al. Aug 2015 B2
9123117 Ciurea et al. Sep 2015 B2
9123118 Ciurea et al. Sep 2015 B2
9124815 Venkataraman et al. Sep 2015 B2
9124831 Mullis Sep 2015 B2
9124864 Mullis Sep 2015 B2
9128228 Duparre Sep 2015 B2
9129183 Venkataraman et al. Sep 2015 B2
9129377 Ciurea et al. Sep 2015 B2
9143711 McMahon Sep 2015 B2
9147254 Ciurea et al. Sep 2015 B2
9185276 Rodda et al. Nov 2015 B2
9188765 Venkataraman et al. Nov 2015 B2
9191580 Venkataraman et al. Nov 2015 B2
9197821 McMahon Nov 2015 B2
9210392 Nisenzon et al. Dec 2015 B2
9214013 Venkataraman et al. Dec 2015 B2
9235898 Venkataraman et al. Jan 2016 B2
9235900 Ciurea et al. Jan 2016 B2
9240049 Ciurea Jan 2016 B2
9253380 Venkataraman et al. Feb 2016 B2
9256974 Hines Feb 2016 B1
9264592 Rodda et al. Feb 2016 B2
9264610 Duparre Feb 2016 B2
9361662 Lelescu et al. Jun 2016 B2
9374512 Venkataraman et al. Jun 2016 B2
9412206 McMahon et al. Aug 2016 B2
9413953 Maeda Aug 2016 B2
9426343 Rodda et al. Aug 2016 B2
9426361 Venkataraman et al. Aug 2016 B2
9438888 Venkataraman et al. Sep 2016 B2
9445003 Lelescu Sep 2016 B1
9456134 Venkataraman et al. Sep 2016 B2
9456196 Kim et al. Sep 2016 B2
9462164 Venkataraman et al. Oct 2016 B2
9485496 Venkataraman et al. Nov 2016 B2
9497370 Venkataraman et al. Nov 2016 B2
9497429 Mullis et al. Nov 2016 B2
9516222 Duparre et al. Dec 2016 B2
9519972 Venkataraman et al. Dec 2016 B2
9521319 Rodda et al. Dec 2016 B2
9521416 McMahon et al. Dec 2016 B1
9536166 Venkataraman et al. Jan 2017 B2
9576369 Venkataraman et al. Feb 2017 B2
9578237 Duparre et al. Feb 2017 B2
9578259 Molina Feb 2017 B2
9602805 Venkataraman et al. Mar 2017 B2
9633442 Venkataraman et al. Apr 2017 B2
9635274 Lin et al. Apr 2017 B2
9638883 Duparre May 2017 B1
9661310 Deng et al. May 2017 B2
9706132 Nisenzon et al. Jul 2017 B2
9712759 Venkataraman et al. Jul 2017 B2
9733486 Lelescu et al. Aug 2017 B2
9741118 Mullis Aug 2017 B2
9743051 Venkataraman et al. Aug 2017 B2
9749547 Venkataraman et al. Aug 2017 B2
9749568 McMahon Aug 2017 B2
9754422 McMahon et al. Sep 2017 B2
9766380 Duparre et al. Sep 2017 B2
9769365 Jannard Sep 2017 B1
9774789 Ciurea et al. Sep 2017 B2
9774831 Venkataraman et al. Sep 2017 B2
9787911 McMahon et al. Oct 2017 B2
9794476 Nayar et al. Oct 2017 B2
9800856 Venkataraman et al. Oct 2017 B2
9800859 Venkataraman et al. Oct 2017 B2
9807382 Duparre et al. Oct 2017 B2
9811753 Venkataraman et al. Nov 2017 B2
9813616 Lelescu et al. Nov 2017 B2
9813617 Venkataraman et al. Nov 2017 B2
9858673 Ciurea et al. Jan 2018 B2
9864921 Venkataraman et al. Jan 2018 B2
9888194 Duparre Feb 2018 B2
9898856 Yang et al. Feb 2018 B2
9917998 Venkataraman et al. Mar 2018 B2
9924092 Rodda et al. Mar 2018 B2
9936148 McMahon Apr 2018 B2
9955070 Lelescu et al. Apr 2018 B2
9986224 Mullis May 2018 B2
10009538 Venkataraman et al. Jun 2018 B2
10019816 Venkataraman et al. Jul 2018 B2
10027901 Venkataraman et al. Jul 2018 B2
10089740 Srikanth et al. Oct 2018 B2
10091405 Molina Oct 2018 B2
10119808 Venkataraman et al. Nov 2018 B2
10122993 Venkataraman et al. Nov 2018 B2
10142560 Venkataraman et al. Nov 2018 B2
10182216 Mullis et al. Jan 2019 B2
10275676 Venkataraman et al. Apr 2019 B2
10306120 Duparre May 2019 B2
10311649 McMohan et al. Jun 2019 B2
10334241 Duparre et al. Jun 2019 B2
10366472 Lelescu et al. Jul 2019 B2
20010005225 Clark et al. Jun 2001 A1
20010019621 Hanna et al. Sep 2001 A1
20010028038 Hamaguchi et al. Oct 2001 A1
20010038387 Tomooka et al. Nov 2001 A1
20020012056 Trevino Jan 2002 A1
20020015536 Warren Feb 2002 A1
20020027608 Johnson et al. Mar 2002 A1
20020028014 Ono Mar 2002 A1
20020039438 Mori et al. Apr 2002 A1
20020057845 Fossum May 2002 A1
20020061131 Sawhney et al. May 2002 A1
20020063807 Margulis May 2002 A1
20020075450 Aratani Jun 2002 A1
20020087403 Meyers et al. Jul 2002 A1
20020089596 Suda Jul 2002 A1
20020094027 Sato et al. Jul 2002 A1
20020101528 Lee Aug 2002 A1
20020113867 Takigawa et al. Aug 2002 A1
20020113888 Sonoda et al. Aug 2002 A1
20020118113 Oku et al. Aug 2002 A1
20020120634 Min et al. Aug 2002 A1
20020122113 Foote et al. Sep 2002 A1
20020163054 Suda et al. Nov 2002 A1
20020167537 Trajkovic Nov 2002 A1
20020171666 Endo et al. Nov 2002 A1
20020177054 Saitoh et al. Nov 2002 A1
20020190991 Efran et al. Dec 2002 A1
20020195548 Dowski, Jr. et al. Dec 2002 A1
20030025227 Daniell Feb 2003 A1
20030086079 Barth et al. May 2003 A1
20030124763 Fan et al. Jul 2003 A1
20030140347 Varsa Jul 2003 A1
20030156189 Utsumi et al. Aug 2003 A1
20030179418 Wengender et al. Sep 2003 A1
20030188659 Merry et al. Oct 2003 A1
20030190072 Adkins et al. Oct 2003 A1
20030198377 Ng Oct 2003 A1
20030211405 Venkataraman Nov 2003 A1
20040003409 Berstis Jan 2004 A1
20040008271 Hagimori et al. Jan 2004 A1
20040012689 Tinnerino Jan 2004 A1
20040027358 Nakao Feb 2004 A1
20040047274 Amanai Mar 2004 A1
20040050104 Ghosh et al. Mar 2004 A1
20040056966 Schechner et al. Mar 2004 A1
20040061787 Liu et al. Apr 2004 A1
20040066454 Otani et al. Apr 2004 A1
20040071367 Irani et al. Apr 2004 A1
20040075654 Hsiao et al. Apr 2004 A1
20040096119 Williams May 2004 A1
20040100570 Shizukuishi May 2004 A1
20040105021 Hu et al. Jun 2004 A1
20040114807 Lelescu et al. Jun 2004 A1
20040141659 Zhang Jul 2004 A1
20040151401 Sawhney et al. Aug 2004 A1
20040165090 Ning Aug 2004 A1
20040169617 Yelton et al. Sep 2004 A1
20040170340 Tipping et al. Sep 2004 A1
20040174439 Upton Sep 2004 A1
20040179008 Gordon et al. Sep 2004 A1
20040179834 Szajewski Sep 2004 A1
20040196379 Chen et al. Oct 2004 A1
20040207600 Zhang et al. Oct 2004 A1
20040207836 Chhibber et al. Oct 2004 A1
20040213449 Safaee-Rad et al. Oct 2004 A1
20040218809 Blake et al. Nov 2004 A1
20040234873 Venkataraman Nov 2004 A1
20040239782 Equitz et al. Dec 2004 A1
20040239885 Jaynes et al. Dec 2004 A1
20040240052 Minefuji et al. Dec 2004 A1
20040251509 Choi Dec 2004 A1
20040264806 Herley Dec 2004 A1
20050006477 Patel Jan 2005 A1
20050007461 Chou et al. Jan 2005 A1
20050009313 Suzuki et al. Jan 2005 A1
20050010621 Pinto et al. Jan 2005 A1
20050012035 Miller Jan 2005 A1
20050036778 DeMonte Feb 2005 A1
20050047678 Jones et al. Mar 2005 A1
20050048690 Yamamoto Mar 2005 A1
20050068436 Fraenkel et al. Mar 2005 A1
20050083531 Millerd et al. Apr 2005 A1
20050084179 Hanna et al. Apr 2005 A1
20050128509 Tokkonen et al. Jun 2005 A1
20050128595 Shimizu Jun 2005 A1
20050132098 Sonoda et al. Jun 2005 A1
20050134698 Schroeder Jun 2005 A1
20050134699 Nagashima Jun 2005 A1
20050134712 Gruhlke et al. Jun 2005 A1
20050147277 Higaki et al. Jul 2005 A1
20050151759 Gonzalez-Banos et al. Jul 2005 A1
20050168924 Wu et al. Aug 2005 A1
20050175257 Kuroki Aug 2005 A1
20050185711 Pfister et al. Aug 2005 A1
20050205785 Hornback et al. Sep 2005 A1
20050219264 Shum et al. Oct 2005 A1
20050219363 Kohler et al. Oct 2005 A1
20050224843 Boemler Oct 2005 A1
20050225654 Feldman et al. Oct 2005 A1
20050265633 Piacentino et al. Dec 2005 A1
20050275946 Choo et al. Dec 2005 A1
20050286612 Takanashi Dec 2005 A1
20050286756 Hong et al. Dec 2005 A1
20060002635 Nestares et al. Jan 2006 A1
20060007331 Izumi et al. Jan 2006 A1
20060013318 Webb et al. Jan 2006 A1
20060018509 Miyoshi Jan 2006 A1
20060023197 Joel Feb 2006 A1
20060023314 Boettiger et al. Feb 2006 A1
20060028476 Sobel et al. Feb 2006 A1
20060029270 Berestov et al. Feb 2006 A1
20060029271 Miyoshi et al. Feb 2006 A1
20060033005 Jerdev et al. Feb 2006 A1
20060034003 Zalevsky Feb 2006 A1
20060034531 Poon et al. Feb 2006 A1
20060035415 Wood Feb 2006 A1
20060038891 Okutomi et al. Feb 2006 A1
20060039611 Rother et al. Feb 2006 A1
20060046204 Ono et al. Mar 2006 A1
20060049930 Zruya et al. Mar 2006 A1
20060050980 Kohashi et al. Mar 2006 A1
20060054780 Garrood et al. Mar 2006 A1
20060054782 Olsen et al. Mar 2006 A1
20060055811 Frtiz et al. Mar 2006 A1
20060069478 Iwama Mar 2006 A1
20060072029 Miyatake et al. Apr 2006 A1
20060087747 Ohzawa et al. Apr 2006 A1
20060098888 Morishita May 2006 A1
20060103754 Wenstrand et al. May 2006 A1
20060125936 Gruhike et al. Jun 2006 A1
20060138322 Costello et al. Jun 2006 A1
20060152803 Provitola Jul 2006 A1
20060157640 Perlman et al. Jul 2006 A1
20060159369 Young Jul 2006 A1
20060176566 Boettiger et al. Aug 2006 A1
20060187338 May et al. Aug 2006 A1
20060197937 Bamji et al. Sep 2006 A1
20060203100 Ajito et al. Sep 2006 A1
20060203113 Wada et al. Sep 2006 A1
20060210146 Gu Sep 2006 A1
20060210186 Berkner Sep 2006 A1
20060214085 Olsen Sep 2006 A1
20060221250 Rossbach et al. Oct 2006 A1
20060239549 Kelly et al. Oct 2006 A1
20060243889 Farnworth et al. Nov 2006 A1
20060251410 Trutna Nov 2006 A1
20060274174 Tewinkle Dec 2006 A1
20060278948 Yamaguchi et al. Dec 2006 A1
20060279648 Senba et al. Dec 2006 A1
20060289772 Johnson et al. Dec 2006 A1
20070002159 Olsen et al. Jan 2007 A1
20070008575 Yu et al. Jan 2007 A1
20070009150 Suwa Jan 2007 A1
20070024614 Tam Feb 2007 A1
20070030356 Yea et al. Feb 2007 A1
20070035707 Margulis Feb 2007 A1
20070036427 Nakamura et al. Feb 2007 A1
20070040828 Zalevsky et al. Feb 2007 A1
20070040922 McKee et al. Feb 2007 A1
20070041391 Lin et al. Feb 2007 A1
20070052825 Cho Mar 2007 A1
20070083114 Yang et al. Apr 2007 A1
20070085917 Kobayashi Apr 2007 A1
20070092245 Bazakos et al. Apr 2007 A1
20070102622 Olsen et al. May 2007 A1
20070126898 Feldman et al. Jun 2007 A1
20070127831 Venkataraman Jun 2007 A1
20070139333 Sato et al. Jun 2007 A1
20070140685 Wu Jun 2007 A1
20070146503 Shiraki Jun 2007 A1
20070146511 Kinoshita et al. Jun 2007 A1
20070153335 Hosaka Jul 2007 A1
20070158427 Zhu et al. Jul 2007 A1
20070159541 Sparks et al. Jul 2007 A1
20070160310 Tanida et al. Jul 2007 A1
20070165931 Higaki Jul 2007 A1
20070171290 Kroger Jul 2007 A1
20070177004 Kolehmainen et al. Aug 2007 A1
20070182843 Shimamura et al. Aug 2007 A1
20070201859 Sarrat Aug 2007 A1
20070206241 Smith et al. Sep 2007 A1
20070211164 Olsen et al. Sep 2007 A1
20070216765 Wong et al. Sep 2007 A1
20070225600 Weibrecht et al. Sep 2007 A1
20070228256 Mentzer Oct 2007 A1
20070236595 Pan et al. Oct 2007 A1
20070242141 Ciurea Oct 2007 A1
20070247517 Zhang et al. Oct 2007 A1
20070257184 Olsen et al. Nov 2007 A1
20070258006 Olsen et al. Nov 2007 A1
20070258706 Raskar et al. Nov 2007 A1
20070263113 Baek et al. Nov 2007 A1
20070263114 Gurevich et al. Nov 2007 A1
20070268374 Robinson Nov 2007 A1
20070296721 Chang et al. Dec 2007 A1
20070296832 Ota et al. Dec 2007 A1
20070296835 Olsen Dec 2007 A1
20070296847 Chang et al. Dec 2007 A1
20070297696 Hamza Dec 2007 A1
20080006859 Mionetto et al. Jan 2008 A1
20080019611 Larkin et al. Jan 2008 A1
20080024683 Damera-Venkata et al. Jan 2008 A1
20080025649 Liu et al. Jan 2008 A1
20080030592 Border et al. Feb 2008 A1
20080030597 Olsen et al. Feb 2008 A1
20080043095 Vetro et al. Feb 2008 A1
20080043096 Vetro et al. Feb 2008 A1
20080054518 Ra et al. Mar 2008 A1
20080056302 Erdal et al. Mar 2008 A1
20080062164 Bassi et al. Mar 2008 A1
20080079805 Takagi et al. Apr 2008 A1
20080080028 Bakin et al. Apr 2008 A1
20080084486 Enge et al. Apr 2008 A1
20080088793 Sverdrup et al. Apr 2008 A1
20080095523 Schilling-Benz et al. Apr 2008 A1
20080099804 Venezia et al. May 2008 A1
20080106620 Sawachi May 2008 A1
20080112059 Choi et al. May 2008 A1
20080112635 Kondo et al. May 2008 A1
20080117289 Schowengerdt et al. May 2008 A1
20080118241 Tekolste et al. May 2008 A1
20080131019 Ng Jun 2008 A1
20080131107 Ueno Jun 2008 A1
20080151097 Chen et al. Jun 2008 A1
20080152215 Horie et al. Jun 2008 A1
20080152296 Oh et al. Jun 2008 A1
20080156991 Hu et al. Jul 2008 A1
20080158259 Kempf et al. Jul 2008 A1
20080158375 Kakkori et al. Jul 2008 A1
20080158698 Chang et al. Jul 2008 A1
20080165257 Boettiger Jul 2008 A1
20080174670 Olsen et al. Jul 2008 A1
20080187305 Raskar et al. Aug 2008 A1
20080193026 Horie et al. Aug 2008 A1
20080211737 Kim et al. Sep 2008 A1
20080218610 Chapman et al. Sep 2008 A1
20080218611 Parulski et al. Sep 2008 A1
20080218612 Border et al. Sep 2008 A1
20080218613 Janson et al. Sep 2008 A1
20080219654 Border et al. Sep 2008 A1
20080239116 Smith Oct 2008 A1
20080240598 Hasegawa Oct 2008 A1
20080247638 Tanida et al. Oct 2008 A1
20080247653 Moussavi et al. Oct 2008 A1
20080272416 Yun Nov 2008 A1
20080273751 Yuan et al. Nov 2008 A1
20080278591 Barna et al. Nov 2008 A1
20080278610 Boettiger Nov 2008 A1
20080284880 Numata Nov 2008 A1
20080291295 Kato et al. Nov 2008 A1
20080298674 Baker et al. Dec 2008 A1
20080310501 Ward et al. Dec 2008 A1
20090027543 Kanehiro Jan 2009 A1
20090050946 Duparre et al. Feb 2009 A1
20090052743 Techmer Feb 2009 A1
20090060281 Tanida et al. Mar 2009 A1
20090066693 Carson Mar 2009 A1
20090079862 Subbotin Mar 2009 A1
20090086074 Li et al. Apr 2009 A1
20090091645 Trimeche et al. Apr 2009 A1
20090091806 Inuiya Apr 2009 A1
20090092363 Daum et al. Apr 2009 A1
20090096050 Park Apr 2009 A1
20090102956 Georgiev Apr 2009 A1
20090103792 Rahn et al. Apr 2009 A1
20090109306 Shan Apr 2009 A1
20090127430 Hirasawa et al. May 2009 A1
20090128644 Camp et al. May 2009 A1
20090128833 Yahav May 2009 A1
20090129667 Ho et al. May 2009 A1
20090140131 Utagawa et al. Jun 2009 A1
20090141933 Wagg Jun 2009 A1
20090147919 Goto et al. Jun 2009 A1
20090152664 Klem et al. Jun 2009 A1
20090167922 Perlman et al. Jul 2009 A1
20090167934 Gupta Jul 2009 A1
20090175349 Ye et al. Jul 2009 A1
20090179142 Duparre et al. Jul 2009 A1
20090180021 Kikuchi et al. Jul 2009 A1
20090200622 Tai et al. Aug 2009 A1
20090201371 Matsuda et al. Aug 2009 A1
20090207235 Francini et al. Aug 2009 A1
20090219435 Yuan et al. Sep 2009 A1
20090225203 Tanida et al. Sep 2009 A1
20090237520 Kaneko et al. Sep 2009 A1
20090245573 Saptharishi et al. Oct 2009 A1
20090256947 Ciurea et al. Oct 2009 A1
20090263017 Tanbakuchi Oct 2009 A1
20090268192 Koenck et al. Oct 2009 A1
20090268970 Babacan et al. Oct 2009 A1
20090268983 Stone et al. Oct 2009 A1
20090273663 Yoshida et al. Nov 2009 A1
20090274387 Jin Nov 2009 A1
20090279800 Uetani et al. Nov 2009 A1
20090284651 Srinivasan Nov 2009 A1
20090290811 Imai Nov 2009 A1
20090297056 Lelescu et al. Dec 2009 A1
20090302205 Olsen et al. Dec 2009 A9
20090317061 Jung et al. Dec 2009 A1
20090322876 Lee et al. Dec 2009 A1
20090323195 Hembree et al. Dec 2009 A1
20090323206 Oliver et al. Dec 2009 A1
20090324118 Maslov et al. Dec 2009 A1
20100002126 Wenstrand et al. Jan 2010 A1
20100002313 Duparre et al. Jan 2010 A1
20100002314 Duparre Jan 2010 A1
20100007714 Kim et al. Jan 2010 A1
20100013927 Nixon Jan 2010 A1
20100044815 Chang et al. Feb 2010 A1
20100045809 Packard Feb 2010 A1
20100053342 Hwang et al. Mar 2010 A1
20100053347 Agarwala et al. Mar 2010 A1
20100053600 Tanida et al. Mar 2010 A1
20100060746 Olsen et al. Mar 2010 A9
20100073463 Momonoi et al. Mar 2010 A1
20100074532 Gordon et al. Mar 2010 A1
20100085351 Deb et al. Apr 2010 A1
20100085425 Tan Apr 2010 A1
20100086227 Sun et al. Apr 2010 A1
20100091389 Henriksen et al. Apr 2010 A1
20100097491 Farina et al. Apr 2010 A1
20100103175 Okutomi et al. Apr 2010 A1
20100103259 Tanida et al. Apr 2010 A1
20100103308 Butterfield et al. Apr 2010 A1
20100111444 Coffman May 2010 A1
20100118127 Nam et al. May 2010 A1
20100128145 Pitts et al. May 2010 A1
20100129048 Pitts et al. May 2010 A1
20100133230 Henriksen et al. Jun 2010 A1
20100133418 Sargent et al. Jun 2010 A1
20100141802 Knight et al. Jun 2010 A1
20100142828 Chang et al. Jun 2010 A1
20100142839 Lakbecker Jun 2010 A1
20100157073 Kondo et al. Jun 2010 A1
20100165152 Lim Jul 2010 A1
20100166410 Chang et al. Jul 2010 A1
20100171866 Brady et al. Jul 2010 A1
20100177411 Hegde et al. Jul 2010 A1
20100182406 Benitez Jul 2010 A1
20100194860 Mentz et al. Aug 2010 A1
20100194901 van Hoorebeke et al. Aug 2010 A1
20100195716 Klein et al. Aug 2010 A1
20100201809 Oyama et al. Aug 2010 A1
20100201834 Maruyama et al. Aug 2010 A1
20100202054 Niederer Aug 2010 A1
20100202683 Robinson Aug 2010 A1
20100208100 Olsen et al. Aug 2010 A9
20100214423 Ogawa Aug 2010 A1
20100220212 Perlman et al. Sep 2010 A1
20100223237 Mishra et al. Sep 2010 A1
20100225740 Jung et al. Sep 2010 A1
20100231285 Boomer et al. Sep 2010 A1
20100238327 Griffith et al. Sep 2010 A1
20100244165 Lake et al. Sep 2010 A1
20100245684 Xiao et al. Sep 2010 A1
20100254627 Panahpour Tehrani et al. Oct 2010 A1
20100259610 Petersen et al. Oct 2010 A1
20100265346 Iizuka Oct 2010 A1
20100265381 Yamamoto et al. Oct 2010 A1
20100265385 Knight et al. Oct 2010 A1
20100277629 Tanaka Nov 2010 A1
20100281070 Chan et al. Nov 2010 A1
20100289941 Ito et al. Nov 2010 A1
20100290483 Park et al. Nov 2010 A1
20100302423 Adams, Jr. et al. Dec 2010 A1
20100309292 Ho et al. Dec 2010 A1
20100309368 Choi et al. Dec 2010 A1
20100321595 Chiu et al. Dec 2010 A1
20100321640 Yeh et al. Dec 2010 A1
20100329556 Mitarai et al. Dec 2010 A1
20100329582 Albu et al. Dec 2010 A1
20110001037 Tewinkle Jan 2011 A1
20110018973 Takayama Jan 2011 A1
20110019048 Raynor et al. Jan 2011 A1
20110019243 Constant, Jr. et al. Jan 2011 A1
20110031381 Tay et al. Feb 2011 A1
20110032341 Ignatov et al. Feb 2011 A1
20110032370 Ludwig Feb 2011 A1
20110033129 Robinson Feb 2011 A1
20110038536 Gong Feb 2011 A1
20110043604 Peleg et al. Feb 2011 A1
20110043613 Rohaly et al. Feb 2011 A1
20110043661 Podoleanu Feb 2011 A1
20110043665 Ogasahara Feb 2011 A1
20110043668 McKinnon et al. Feb 2011 A1
20110044502 Liu et al. Feb 2011 A1
20110051255 Lee et al. Mar 2011 A1
20110055729 Mason et al. Mar 2011 A1
20110064327 Dagher et al. Mar 2011 A1
20110069189 Venkataraman et al. Mar 2011 A1
20110080487 Venkataraman et al. Apr 2011 A1
20110085028 Samadani et al. Apr 2011 A1
20110090217 Mashitani et al. Apr 2011 A1
20110108708 Olsen et al. May 2011 A1
20110115886 Nguyen May 2011 A1
20110121421 Charbon May 2011 A1
20110122308 Duparre May 2011 A1
20110128393 Tavi et al. Jun 2011 A1
20110128412 Milnes et al. Jun 2011 A1
20110129165 Lim et al. Jun 2011 A1
20110141309 Nagashima et al. Jun 2011 A1
20110142138 Tian et al. Jun 2011 A1
20110149408 Hahgholt et al. Jun 2011 A1
20110149409 Haugholt et al. Jun 2011 A1
20110150321 Cheong et al. Jun 2011 A1
20110153248 Gu et al. Jun 2011 A1
20110157321 Nakajima et al. Jun 2011 A1
20110157451 Chang Jun 2011 A1
20110169994 DiFrancesco et al. Jul 2011 A1
20110176020 Chang Jul 2011 A1
20110181797 Galstian et al. Jul 2011 A1
20110193944 Lian et al. Aug 2011 A1
20110199458 Hayasaka et al. Aug 2011 A1
20110200319 Kravitz et al. Aug 2011 A1
20110206291 Kashani et al. Aug 2011 A1
20110207074 Hall-Holt et al. Aug 2011 A1
20110211068 Yokota Sep 2011 A1
20110211077 Nayar et al. Sep 2011 A1
20110211824 Georgiev et al. Sep 2011 A1
20110221599 Högasten Sep 2011 A1
20110221658 Haddick et al. Sep 2011 A1
20110221939 Jerdev Sep 2011 A1
20110221950 Oostra Sep 2011 A1
20110222757 Yeatman, Jr. et al. Sep 2011 A1
20110228142 Brueckner Sep 2011 A1
20110228144 Tian et al. Sep 2011 A1
20110234841 Akeley et al. Sep 2011 A1
20110241234 Duparre Oct 2011 A1
20110242342 Goma et al. Oct 2011 A1
20110242355 Goma et al. Oct 2011 A1
20110242356 Aleksic et al. Oct 2011 A1
20110243428 Das Gupta et al. Oct 2011 A1
20110255592 Sung Oct 2011 A1
20110255745 Hodder et al. Oct 2011 A1
20110261993 Weiming et al. Oct 2011 A1
20110267264 McCarthy et al. Nov 2011 A1
20110267348 Lin Nov 2011 A1
20110273531 Ito et al. Nov 2011 A1
20110274175 Sumitomo Nov 2011 A1
20110274366 Tardif Nov 2011 A1
20110279705 Kuang et al. Nov 2011 A1
20110279721 McMahon Nov 2011 A1
20110285701 Chen et al. Nov 2011 A1
20110285866 Bhrugumalla et al. Nov 2011 A1
20110285910 Bamji et al. Nov 2011 A1
20110292216 Fergus et al. Dec 2011 A1
20110298898 Jung et al. Dec 2011 A1
20110298917 Yanagita Dec 2011 A1
20110300929 Tardif et al. Dec 2011 A1
20110310980 Mathew Dec 2011 A1
20110316968 Taguchi et al. Dec 2011 A1
20110317766 Lim, II et al. Dec 2011 A1
20120012748 Pain et al. Jan 2012 A1
20120014456 Martinez Bauza et al. Jan 2012 A1
20120019530 Baker Jan 2012 A1
20120019700 Gaber Jan 2012 A1
20120023456 Sun et al. Jan 2012 A1
20120026297 Sato Feb 2012 A1
20120026342 Yu et al. Feb 2012 A1
20120026366 Golan et al. Feb 2012 A1
20120026451 Nystrom Feb 2012 A1
20120038745 Yu et al. Feb 2012 A1
20120039525 Tian et al. Feb 2012 A1
20120044249 Mashitani et al. Feb 2012 A1
20120044372 Côtéet al. Feb 2012 A1
20120051624 Ando Mar 2012 A1
20120056982 Katz et al. Mar 2012 A1
20120057040 Park et al. Mar 2012 A1
20120062697 Treado et al. Mar 2012 A1
20120062702 Jiang et al. Mar 2012 A1
20120062756 Tian Mar 2012 A1
20120069235 Imai Mar 2012 A1
20120081519 Goma Apr 2012 A1
20120086803 Malzbender et al. Apr 2012 A1
20120105590 Fukumoto et al. May 2012 A1
20120105654 Kwatra et al. May 2012 A1
20120105691 Waqas et al. May 2012 A1
20120113232 Joblove May 2012 A1
20120113318 Galstian et al. May 2012 A1
20120113413 Miahczylowicz-Wolski et al. May 2012 A1
20120114224 Xu et al. May 2012 A1
20120120264 Lee et al. May 2012 A1
20120127275 Von Zitzewitz et al. May 2012 A1
20120147139 Li et al. Jun 2012 A1
20120147205 Lelescu et al. Jun 2012 A1
20120153153 Chang et al. Jun 2012 A1
20120154551 Inoue Jun 2012 A1
20120155830 Sasaki et al. Jun 2012 A1
20120163672 McKinnon Jun 2012 A1
20120163725 Fukuhara Jun 2012 A1
20120169433 Mullins et al. Jul 2012 A1
20120170134 Bolis et al. Jul 2012 A1
20120176479 Mayhew et al. Jul 2012 A1
20120176481 Lukk et al. Jul 2012 A1
20120188235 Wu et al. Jul 2012 A1
20120188341 Klein Gunnewiek et al. Jul 2012 A1
20120188389 Lin et al. Jul 2012 A1
20120188420 Black et al. Jul 2012 A1
20120188634 Kubala et al. Jul 2012 A1
20120198677 Duparre Aug 2012 A1
20120200669 Lai Aug 2012 A1
20120200726 Bugnariu Aug 2012 A1
20120200734 Tang Aug 2012 A1
20120206582 DiCarlo et al. Aug 2012 A1
20120219236 Ali et al. Aug 2012 A1
20120224083 Jovanovski et al. Sep 2012 A1
20120229602 Chen et al. Sep 2012 A1
20120229628 Ishiyama et al. Sep 2012 A1
20120237114 Park et al. Sep 2012 A1
20120249550 Akeley et al. Oct 2012 A1
20120249750 Izzat et al. Oct 2012 A1
20120249836 Ali et al. Oct 2012 A1
20120249853 Krolczyk et al. Oct 2012 A1
20120250990 Bocirnea Oct 2012 A1
20120262601 Choi et al. Oct 2012 A1
20120262607 Shimura et al. Oct 2012 A1
20120268574 Gidon et al. Oct 2012 A1
20120274626 Hsieh Nov 2012 A1
20120287291 McMahon et al. Nov 2012 A1
20120290257 Hodge et al. Nov 2012 A1
20120293489 Chen et al. Nov 2012 A1
20120293624 Chen et al. Nov 2012 A1
20120293695 Tanaka Nov 2012 A1
20120307093 Miyoshi Dec 2012 A1
20120307099 Yahata et al. Dec 2012 A1
20120314033 Lee et al. Dec 2012 A1
20120314937 Kim et al. Dec 2012 A1
20120327222 Ng et al. Dec 2012 A1
20130002828 Ding et al. Jan 2013 A1
20130003184 Duparre Jan 2013 A1
20130010073 Do et al. Jan 2013 A1
20130016245 Yuba Jan 2013 A1
20130016885 Tsujimoto et al. Jan 2013 A1
20130022111 Chen et al. Jan 2013 A1
20130027580 Olsen et al. Jan 2013 A1
20130033579 Wajs Feb 2013 A1
20130033585 Li et al. Feb 2013 A1
20130038696 Ding et al. Feb 2013 A1
20130047396 Au et al. Feb 2013 A1
20130050504 Safaee-Rad et al. Feb 2013 A1
20130050526 Keelan Feb 2013 A1
20130057710 McMahon Mar 2013 A1
20130070060 Chatterjee Mar 2013 A1
20130076967 Brunner et al. Mar 2013 A1
20130077859 Stauder et al. Mar 2013 A1
20130077880 Venkataraman et al. Mar 2013 A1
20130077882 Venkataraman et al. Mar 2013 A1
20130083172 Baba Apr 2013 A1
20130088489 Schmeitz et al. Apr 2013 A1
20130088637 Duparre Apr 2013 A1
20130093842 Yahata Apr 2013 A1
20130107061 Kumar et al. May 2013 A1
20130113888 Koguchi May 2013 A1
20130113899 Morohoshi et al. May 2013 A1
20130113939 Strandemar May 2013 A1
20130120536 Song et al. May 2013 A1
20130120605 Georgiev et al. May 2013 A1
20130121559 Hu May 2013 A1
20130128068 Georgiev et al. May 2013 A1
20130128069 Georgiev et al. May 2013 A1
20130128087 Georgiev et al. May 2013 A1
20130128121 Agarwala et al. May 2013 A1
20130135315 Bares May 2013 A1
20130135448 Nagumo et al. May 2013 A1
20130147979 McMahon et al. Jun 2013 A1
20130155050 Rastogi et al. Jun 2013 A1
20130162641 Zhang et al. Jun 2013 A1
20130169754 Aronsson et al. Jul 2013 A1
20130176394 Tian et al. Jul 2013 A1
20130208138 Li Aug 2013 A1
20130215108 McMahon et al. Aug 2013 A1
20130215231 Hiramoto et al. Aug 2013 A1
20130222556 Shimada Aug 2013 A1
20130222656 Kaneko Aug 2013 A1
20130223759 Nishiyama et al. Aug 2013 A1
20130229540 Farina et al. Sep 2013 A1
20130230237 Schlosser et al. Sep 2013 A1
20130250123 Zhang et al. Sep 2013 A1
20130250150 Malone et al. Sep 2013 A1
20130258067 Zhang et al. Oct 2013 A1
20130259317 Gaddy Oct 2013 A1
20130265459 Duparre et al. Oct 2013 A1
20130274596 Azizian et al. Oct 2013 A1
20130274923 By et al. Oct 2013 A1
20130286236 Mankowski Oct 2013 A1
20130293760 Nisenzon et al. Nov 2013 A1
20130308197 Duparre Nov 2013 A1
20130321581 El-ghoroury et al. Dec 2013 A1
20130321589 Kirk et al. Dec 2013 A1
20130335598 Gustaysson et al. Dec 2013 A1
20140002674 Duparre et al. Jan 2014 A1
20140002675 Duparre et al. Jan 2014 A1
20140009586 McNamer et al. Jan 2014 A1
20140013273 Ng Jan 2014 A1
20140037137 Broaddus et al. Feb 2014 A1
20140037140 Benhimane et al. Feb 2014 A1
20140043507 Wang et al. Feb 2014 A1
20140059462 Wernersson Feb 2014 A1
20140076336 Clayton et al. Mar 2014 A1
20140078333 Miao Mar 2014 A1
20140079336 Venkataraman et al. Mar 2014 A1
20140081454 Nuyujukian et al. Mar 2014 A1
20140085502 Lin et al. Mar 2014 A1
20140092281 Nisenzon et al. Apr 2014 A1
20140098266 Nayar et al. Apr 2014 A1
20140098267 Tian et al. Apr 2014 A1
20140104490 Hsieh et al. Apr 2014 A1
20140118493 Sali et al. May 2014 A1
20140118584 Lee et al. May 2014 A1
20140125771 Grossmann et al. May 2014 A1
20140132810 McMahon May 2014 A1
20140139642 Ni et al. May 2014 A1
20140139643 Hogasten May 2014 A1
20140140626 Cho et al. May 2014 A1
20140146132 Bagnato et al. May 2014 A1
20140146201 Knight et al. May 2014 A1
20140176592 Wilburn et al. Jun 2014 A1
20140183334 Wang et al. Jul 2014 A1
20140186045 Poddar et al. Jul 2014 A1
20140192154 Jeong et al. Jul 2014 A1
20140192253 Laroia Jul 2014 A1
20140198188 Izawa Jul 2014 A1
20140204183 Lee et al. Jul 2014 A1
20140218546 McMahon Aug 2014 A1
20140232822 Venkataraman et al. Aug 2014 A1
20140240528 Venkataraman et al. Aug 2014 A1
20140240529 Venkataraman et al. Aug 2014 A1
20140253738 Mullis Sep 2014 A1
20140267243 Venkataraman et al. Sep 2014 A1
20140267286 Duparre Sep 2014 A1
20140267633 Venkataraman et al. Sep 2014 A1
20140267762 Mullis et al. Sep 2014 A1
20140267829 McMahon et al. Sep 2014 A1
20140267890 Lelescu et al. Sep 2014 A1
20140285675 Mullis Sep 2014 A1
20140300706 Song Oct 2014 A1
20140313315 Shoham et al. Oct 2014 A1
20140321712 Ciurea et al. Oct 2014 A1
20140333731 Venkataraman et al. Nov 2014 A1
20140333764 Venkataraman et al. Nov 2014 A1
20140333787 Venkataraman et al. Nov 2014 A1
20140340539 Venkataraman et al. Nov 2014 A1
20140347509 Venkataraman et al. Nov 2014 A1
20140347748 Duparre Nov 2014 A1
20140354773 Venkataraman et al. Dec 2014 A1
20140354843 Venkataraman et al. Dec 2014 A1
20140354844 Venkataraman et al. Dec 2014 A1
20140354853 Venkataraman et al. Dec 2014 A1
20140354854 Venkataraman et al. Dec 2014 A1
20140354855 Venkataraman et al. Dec 2014 A1
20140355870 Venkataraman et al. Dec 2014 A1
20140368662 Venkataraman et al. Dec 2014 A1
20140368683 Venkataraman et al. Dec 2014 A1
20140368684 Venkataraman et al. Dec 2014 A1
20140368685 Venkataraman et al. Dec 2014 A1
20140368686 Duparre Dec 2014 A1
20140369612 Venkataraman et al. Dec 2014 A1
20140369615 Venkataraman et al. Dec 2014 A1
20140376825 Venkataraman et al. Dec 2014 A1
20140376826 Venkataraman et al. Dec 2014 A1
20150002734 Lee Jan 2015 A1
20150003752 Venkataraman et al. Jan 2015 A1
20150003753 Venkataraman et al. Jan 2015 A1
20150009353 Venkataraman et al. Jan 2015 A1
20150009354 Venkataraman et al. Jan 2015 A1
20150009362 Venkataraman et al. Jan 2015 A1
20150015669 Venkataraman et al. Jan 2015 A1
20150035992 Mullis Feb 2015 A1
20150036014 Lelescu et al. Feb 2015 A1
20150036015 Lelescu et al. Feb 2015 A1
20150042766 Ciurea et al. Feb 2015 A1
20150042767 Ciurea et al. Feb 2015 A1
20150042833 Lelescu et al. Feb 2015 A1
20150049915 Ciurea et al. Feb 2015 A1
20150049916 Ciurea et al. Feb 2015 A1
20150049917 Ciurea et al. Feb 2015 A1
20150055884 Venkataraman et al. Feb 2015 A1
20150085073 Bruls et al. Mar 2015 A1
20150085174 Shabtay et al. Mar 2015 A1
20150091900 Yang et al. Apr 2015 A1
20150098079 Montgomery et al. Apr 2015 A1
20150104076 Hayasaka Apr 2015 A1
20150104101 Bryant et al. Apr 2015 A1
20150122411 Rodda et al. May 2015 A1
20150124059 Georgiev et al. May 2015 A1
20150124113 Rodda et al. May 2015 A1
20150124151 Rodda et al. May 2015 A1
20150138346 Venkataraman et al. May 2015 A1
20150146029 Venkataraman et al. May 2015 A1
20150146030 Venkataraman et al. May 2015 A1
20150161798 Venkataraman et al. Jun 2015 A1
20150199793 Venkataraman et al. Jul 2015 A1
20150199841 Venkataraman et al. Jul 2015 A1
20150235476 McMahon et al. Aug 2015 A1
20150243480 Yamada Aug 2015 A1
20150244927 Laroia et al. Aug 2015 A1
20150248744 Hayasaka et al. Sep 2015 A1
20150254868 Srikanth et al. Sep 2015 A1
20150264337 Venkataraman et al. Sep 2015 A1
20150296137 Duparre et al. Oct 2015 A1
20150312455 Venkataraman et al. Oct 2015 A1
20150326852 Duparre et al. Nov 2015 A1
20150332468 Hayasaka et al. Nov 2015 A1
20150373261 Rodda Dec 2015 A1
20160037097 Duparre Feb 2016 A1
20160044252 Molina Feb 2016 A1
20160044257 Venkataraman et al. Feb 2016 A1
20160057332 Ciurea et al. Feb 2016 A1
20160065934 Kaza et al. Mar 2016 A1
20160163051 Mullis Jun 2016 A1
20160165106 Duparre Jun 2016 A1
20160165134 Lelescu et al. Jun 2016 A1
20160165147 Nisenzon et al. Jun 2016 A1
20160165212 Mullis Jun 2016 A1
20160195733 Lelescu et al. Jul 2016 A1
20160198096 McMahon et al. Jul 2016 A1
20160227195 Venkataraman et al. Aug 2016 A1
20160249001 McMahon Aug 2016 A1
20160255333 Nisenzon et al. Sep 2016 A1
20160266284 Duparre et al. Sep 2016 A1
20160267665 Venkataraman et al. Sep 2016 A1
20160267672 Ciurea et al. Sep 2016 A1
20160269626 McMahon Sep 2016 A1
20160269627 McMahon Sep 2016 A1
20160269650 Venkataraman et al. Sep 2016 A1
20160269651 Venkataraman et al. Sep 2016 A1
20160269664 Duparre Sep 2016 A1
20160316140 Nayar et al. Oct 2016 A1
20170006233 Venkataraman et al. Jan 2017 A1
20170048468 Pain et al. Feb 2017 A1
20170053382 Lelescu et al. Feb 2017 A1
20170054901 Venkataraman et al. Feb 2017 A1
20170070672 Rodda et al. Mar 2017 A1
20170070673 Lelescu et al. Mar 2017 A1
20170078568 Venkataraman et al. Mar 2017 A1
20170085845 Venkataraman et al. Mar 2017 A1
20170094243 Venkataraman et al. Mar 2017 A1
20170099465 Mullis et al. Apr 2017 A1
20170163862 Molina Jun 2017 A1
20170178363 Venkataraman et al. Jun 2017 A1
20170187933 Duparre Jun 2017 A1
20170188011 Panescu et al. Jun 2017 A1
20170244960 Ciurea et al. Aug 2017 A1
20170257562 Venkataraman Sep 2017 A1
20170365104 McMahon et al. Dec 2017 A1
20180007284 Venkataraman Jan 2018 A1
20180013945 Ciurea et al. Jan 2018 A1
20180024330 Laroia Jan 2018 A1
20180035057 McMahon et al. Feb 2018 A1
20180040135 Mullis Feb 2018 A1
20180048830 Venkataraman et al. Feb 2018 A1
20180048879 Venkataraman et al. Feb 2018 A1
20180081090 Duparre et al. Mar 2018 A1
20180097993 Nayar et al. Apr 2018 A1
20180109782 Duparre et al. Apr 2018 A1
20180124311 Lelescu et al. May 2018 A1
20180139382 Venkataraman et al. May 2018 A1
20180197035 Venkataraman et al. Jul 2018 A1
20180211402 Ciurea et al. Jul 2018 A1
20180240265 Yang et al. Aug 2018 A1
20180270473 Mullis Sep 2018 A1
20180302554 Lelescu et al. Oct 2018 A1
20180330182 Venkataraman et al. Nov 2018 A1
20190098209 Venkataraman et al. Mar 2019 A1
20190215496 Mullis et al. Jul 2019 A1
Foreign Referenced Citations (202)
Number Date Country
1619358 May 2005 CN
1669332 Sep 2005 CN
1839394 Sep 2006 CN
1985524 Jun 2007 CN
101010619 Aug 2007 CN
101064780 Oct 2007 CN
101102388 Jan 2008 CN
101147392 Mar 2008 CN
201043890 Apr 2008 CN
101427372 May 2009 CN
101606086 Dec 2009 CN
101883291 Nov 2010 CN
102037717 Apr 2011 CN
102375199 Mar 2012 CN
104081414 Oct 2014 CN
104508681 Apr 2015 CN
104662589 May 2015 CN
104685513 Jun 2015 CN
104685860 Jun 2015 CN
104081414 Aug 2017 CN
104662589 Aug 2017 CN
107230236 Oct 2017 CN
107346061 Nov 2017 CN
104685513 Apr 2018 CN
104335246 Sep 2018 CN
602011041799.1 Sep 2017 DE
0677821 Oct 1995 EP
0840502 May 1998 EP
1201407 May 2002 EP
1355274 Oct 2003 EP
1734766 Dec 2006 EP
1243945 Jan 2009 EP
2026563 Feb 2009 EP
2104334 Sep 2009 EP
2244484 Oct 2010 EP
0957642 Apr 2011 EP
2336816 Jun 2011 EP
2339532 Jun 2011 EP
2381418 Oct 2011 EP
2502115 Sep 2012 EP
2652678 Oct 2013 EP
2761534 Aug 2014 EP
2867718 May 2015 EP
2873028 May 2015 EP
2888698 Jul 2015 EP
2888720 Jul 2015 EP
2901671 Aug 2015 EP
2973476 Jan 2016 EP
3066690 Sep 2016 EP
2652678 Sep 2017 EP
2817955 Apr 2018 EP
3328048 May 2018 EP
3075140 Jun 2018 EP
2482022 Jan 2012 GB
2708CHENP2014 Aug 2015 IN
59-025483 Feb 1984 JP
64-037177 Feb 1989 JP
02-285772 Nov 1990 JP
06129851 May 1994 JP
07-015457 Jan 1995 JP
H0756112 Mar 1995 JP
09171075 Jun 1997 JP
09181913 Jul 1997 JP
10253351 Sep 1998 JP
11142609 May 1999 JP
11223708 Aug 1999 JP
11325889 Nov 1999 JP
2000209503 Jul 2000 JP
2001008235 Jan 2001 JP
2001194114 Jul 2001 JP
2001264033 Sep 2001 JP
2001277260 Oct 2001 JP
2001337263 Dec 2001 JP
2002195910 Jul 2002 JP
2002205310 Jul 2002 JP
2002250607 Sep 2002 JP
2002252338 Sep 2002 JP
2003094445 Apr 2003 JP
2003139910 May 2003 JP
2003163938 Jun 2003 JP
2003298920 Oct 2003 JP
2004221585 Aug 2004 JP
2005116022 Apr 2005 JP
2005181460 Jul 2005 JP
2005295381 Oct 2005 JP
2005303694 Oct 2005 JP
2005341569 Dec 2005 JP
2005354124 Dec 2005 JP
2006033228 Feb 2006 JP
2006033493 Feb 2006 JP
2006047944 Feb 2006 JP
2006258930 Sep 2006 JP
2007520107 Jul 2007 JP
2007259136 Oct 2007 JP
2008039852 Feb 2008 JP
2008055908 Mar 2008 JP
2008507874 Mar 2008 JP
2008172735 Jul 2008 JP
2008258885 Oct 2008 JP
2009064421 Mar 2009 JP
2009132010 Jun 2009 JP
2009300268 Dec 2009 JP
2010139288 Jun 2010 JP
2011017764 Jan 2011 JP
2011030184 Feb 2011 JP
2011109484 Jun 2011 JP
2011523538 Aug 2011 JP
2011203238 Oct 2011 JP
2012504805 Feb 2012 JP
2013509022 Mar 2013 JP
2013526801 Jun 2013 JP
2014521117 Aug 2014 JP
2014535191 Dec 2014 JP
2015522178 Aug 2015 JP
2015534734 Dec 2015 JP
2016524125 Aug 2016 JP
6140709 May 2017 JP
2017163550 Sep 2017 JP
2017163587 Sep 2017 JP
2017531976 Oct 2017 JP
1020110097647 Aug 2011 KR
20170063827 Jun 2017 KR
101824672 Feb 2018 KR
101843994 Mar 2018 KR
191151 Jul 2013 SG
11201500910 Oct 2015 SG
200828994 Jul 2008 TW
200939739 Sep 2009 TW
201228382 Jul 2012 TW
I535292 May 2016 TW
1994020875 Jan 1995 WO
2005057922 Jun 2005 WO
2006039906 Apr 2006 WO
2006039906 Apr 2006 WO
2007013250 Feb 2007 WO
2007083579 Jul 2007 WO
2007134137 Nov 2007 WO
2008045198 Apr 2008 WO
2008050904 May 2008 WO
2008108271 Sep 2008 WO
2008108926 Sep 2008 WO
2008150817 Dec 2008 WO
2009073950 Jun 2009 WO
2009151903 Dec 2009 WO
2009157273 Dec 2009 WO
2010037512 Apr 2010 WO
2011008443 Jan 2011 WO
2011026527 Mar 2011 WO
2011046607 Apr 2011 WO
2011055655 May 2011 WO
2011063347 May 2011 WO
2011105814 Sep 2011 WO
2011116203 Sep 2011 WO
2011063347 Oct 2011 WO
2011143501 Nov 2011 WO
2012057619 May 2012 WO
2012057620 May 2012 WO
2012057620 May 2012 WO
2012057621 May 2012 WO
2012057622 May 2012 WO
2012057623 May 2012 WO
2012074361 Jun 2012 WO
2012078126 Jun 2012 WO
2012082904 Jun 2012 WO
2012155119 Nov 2012 WO
2013003276 Jan 2013 WO
2013043751 Mar 2013 WO
2013043761 Mar 2013 WO
2013049699 Apr 2013 WO
2013055960 Apr 2013 WO
2013119706 Aug 2013 WO
2013126578 Aug 2013 WO
2013166215 Nov 2013 WO
2014004134 Jan 2014 WO
2014005123 Jan 2014 WO
2014031795 Feb 2014 WO
2014052974 Apr 2014 WO
2014032020 May 2014 WO
2014078443 May 2014 WO
2014130849 Aug 2014 WO
2014133974 Sep 2014 WO
2014138695 Sep 2014 WO
2014138697 Sep 2014 WO
2014144157 Sep 2014 WO
2014145856 Sep 2014 WO
2014149403 Sep 2014 WO
2014149902 Sep 2014 WO
2014150856 Sep 2014 WO
2014153098 Sep 2014 WO
2014159721 Oct 2014 WO
2014159779 Oct 2014 WO
2014160142 Oct 2014 WO
2014164550 Oct 2014 WO
2014164909 Oct 2014 WO
2014165244 Oct 2014 WO
2014133974 Apr 2015 WO
2015048694 Apr 2015 WO
2015070105 May 2015 WO
2015074078 May 2015 WO
2015081279 Jun 2015 WO
2015134996 Sep 2015 WO
2016054089 Apr 2016 WO
Non-Patent Literature Citations (301)
Entry
US 8,957,977 B2, 02/2015, Venkataraman et al. (withdrawn)
Crabb et al., “Real-time foreground segmentation via range and color imaging”, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, USA, Jun. 23-28, 2008, pp. 1-5.
Debevec et al., “Recovering High Dynamic Range Radiance Maps from Photographs”, Computer Graphics (ACM SIGGRAPH Proceedings), Aug. 16, 1997, 10 pgs.
Do, Minh N. “Immersive Visual Communication with Depth”, Presented at Microsoft Research, Jun. 15, 2011, Retrieved from: http://minhdo.ece.illinois.edu/talks/ImmersiveComm.pdf, 42 pgs.
Do et al., “Immersive Visual Communication”, IEEE Signal Processing Magazine, vol. 28, Issue 1, Jan. 2011, DOI: 10.1109/MSP.2010.939075, Retrieved from: http://minhdo.ece.illinois.edu/publications/ImmerComm_SPM.pdf, pp. 58-66.
Drouin et al., “Fast Multiple-Baseline Stereo with Occlusion”, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05), Ottawa, Ontario, Canada, Jun. 13-16, 2005, pp. 540-547.
Drouin et al., “Geo-Consistency for Wide Multi-Camera Stereo”, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, Jun. 20-25, 2005, pp. 351-358.
Drouin et al., “Improving Border Localization of Multi-Baseline Stereo Using Border-Cut”, International Journal of Computer Vision, Jul. 5, 2006, vol. 83, Issue 3, 8 pgs.
Drulea et al., “Motion Estimation Using the Correlation Transform”, IEEE Transactions on Image Processing, Aug. 2013, vol. 22, No. 8, pp. 3260-3270, first published May 14, 2013.
Duparre et al., “Artificial apposition compound eye fabricated by micro-optics technology”, Applied Optics, Aug. 1, 2004, vol. 43, No. 22, pp. 4303-4310.
Duparre et al., “Artificial compound eye zoom camera”, Bioinspiration & Biomimetics, Nov. 21, 2008, vol. 3, pp. 1-6.
Duparre et al., “Artificial compound eyes—different concepts and their application to ultra flat image acquisition sensors”, MOEMS and Miniaturized Systems IV, Proc. SPIE 5346, Jan. 24, 2004, pp. 89-100.
Duparre et al., “Chirped arrays of refractive ellipsoidal microlenses for aberration correction under oblique incidence”, Optics Express, Dec. 26, 2005, vol. 13, No. 26, pp. 10539-10551.
Duparre et al., “Micro-optical artificial compound eyes”, Bioinspiration & Biomimetics, Apr. 6, 2006, vol. 1, pp. R1-R16.
Duparre et al., “Microoptical artificial compound eyes—from design to experimental verification of two different concepts”, Proc. of SPIE, Optical Design and Engineering II, vol. 5962, Oct. 17, 2005, pp. 59622A-1-59622A-12.
Duparre et al., “Microoptical Artificial Compound Eyes—Two Different Concepts for Compact Imaging Systems”, 11th Microoptics Conference, Oct. 30-Nov. 2, 2005, 2 pgs.
Duparre et al., “Microoptical telescope compound eye”, Optics Express, Feb. 7, 2005, vol. 13, No. 3, pp. 889-903.
Duparre et al., “Micro-optically fabricated artificial apposition compound eye”, Electronic Imaging—Science and Technology, Prod. SPIE 5301, Jan. 2004, pp. 25-33.
Duparre et al., “Novel Optics/Micro-Optics for Miniature Imaging Systems”, Proc. of SPIE, Apr. 21, 2006, vol. 6196, pp. 619607-1-619607-15.
Duparre et al., “Theoretical analysis of an artificial superposition compound eye for application in ultra flat digital image acquisition devices”, Optical Systems Design, Proc. SPIE 5249, Sep. 2003, pp. 408-418.
Duparre et al., “Thin compound-eye camera”, Applied Optics, May 20, 2005, vol. 44, No. 15, pp. 2949-2956.
Duparre et al., “Ultra-Thin Camera Based on Artificial Apposition Compound Eyes”, 10th Microoptics Conference, Sep. 1-3, 2004, 2 pgs.
Eng et al., “Gaze correction for 3D tele-immersive communication system”, IVMSP Workshop, 2013 IEEE 11th. IEEE, Jun. 10, 2013.
Fanaswala, “Regularized Super-Resolution of Multi-View Images”, Retrieved on Nov. 10, 2012 (Nov. 10, 2012). Retrieved from the Internet at URL:<http://www.site.uottawa.ca/-edubois/theses/Fanaswala_thesis.pdf>, 2009, 163 pgs.
Fang et al., “Volume Morphing Methods for Landmark Based 3D Image Deformation”, SPIE vol. 2710, Proc. 1996 SPIE Intl Symposium on Medical Imaging, Newport Beach, CA, Feb. 10, 1996, pp. 404-415.
Farrell et al., “Resolution and Light Sensitivity Tradeoff with Pixel Size”, Proceedings of the SPIE Electronic Imaging 2006 Conference, Feb. 2, 2006, vol. 6069, 8 pgs.
Farsiu et al., “Advances and Challenges in Super-Resolution”, International Journal of Imaging Systems and Technology, Aug. 12, 2004, vol. 14, pp. 47-57.
Farsiu et al., “Fast and Robust Multiframe Super Resolution”, IEEE Transactions on Image Processing, Oct. 2004, published Sep. 3, 2004, vol. 13, No. 10, pp. 1327-1344.
Farsiu et al., “Multiframe Demosaicing and Super-Resolution of Color Images”, IEEE Transactions on Image Processing, Jan. 2006, vol. 15, No. 1, date of publication Dec. 12, 2005, pp. 141-159.
Fecker et al., “Depth Map Compression for Unstructured Lumigraph Rendering”, Proc. SPIE 6077, Proceedings Visual Communications and Image Processing 2006, Jan. 18, 2006, pp. 60770B-1-60770B-8.
Feris et al., “Multi-Flash Stereopsis: Depth Edge Preserving Stereo with Small Baseline Illumination”, IEEE Trans on PAMI, 2006, 31 pgs.
Fife et al., “A 3D Multi-Aperture Image Sensor Architecture”, Custom Integrated Circuits Conference, 2006, CICC '06, IEEE, pp. 281-284.
Fife et al., “A 3MPixel Multi-Aperture Image Sensor with 0.7Mu Pixels in 0.11Mu CMOS”, ISSCC 2008, Session 2, Image Sensors & Technology, 2008, pp. 48-50.
Fischer et al., “Optical System Design”, 2nd Edition, SPIE Press, Feb. 14, 2008, pp. 191-198.
Fischer et al., “Optical System Design”, 2nd Edition, SPIE Press, Feb. 14, 2008, pp. 49-58.
Gastal et al., “Shared Sampling for Real-Time Alpha Matting”, Computer Graphics Forum, EUROGRAPHICS 2010, vol. 29, Issue 2, May 2010, pp. 575-584.
Georgeiv et al., “Light Field Camera Design for Integral View Photography”, Adobe Systems Incorporated, Adobe Technical Report, 2003, 13 pgs.
Georgiev et al., “Light-Field Capture by Multiplexing in the Frequency Domain”, Adobe Systems Incorporated, Adobe Technical Report, 2003, 13 pgs.
Goldman et al., “Video Object Annotation, Navigation, and Composition”, In Proceedings of UIST 2008, Oct. 19-22, 2008, Monterey CA, USA, pp. 3-12.
Gortler et al., “The Lumigraph”, In Proceedings of SIGGRAPH 1996, published Aug. 1, 1996, pp. 43-54.
Gupta et al., “Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images”, 2013 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 23-28, 2013, Portland, OR, USA, pp. 564-571.
Hacohen et al., “Non-Rigid Dense Correspondence with Applications for Image Enhancement”, ACM Transactions on Graphics, vol. 30, No. 4, Aug. 7, 2011, 9 pgs.
Hamilton, “JPEG File Interchange Format, Version 1.02”, Sep. 1, 1992, 9 pgs.
Hardie, “A Fast Image Super-Algorithm Using an Adaptive Wiener Filter”, IEEE Transactions on Image Processing, Dec. 2007, published Nov. 19, 2007, vol. 16, No. 12, pp. 2953-2964.
Hasinoff et al., “Search-and-Replace Editing for Personal Photo Collections”, 2010 International Conference: Computational Photography (ICCP) Mar. 2010, pp. 1-8.
Hernandez-Lopez et al., “Detecting objects using color and depth segmentation with Kinect sensor”, Procedia Technology, vol. 3, Jan. 1, 2012, pp. 196-204, XP055307680, ISSN: 2212-0173, DOI: 10.1016/j.protcy.2012.03.021.
Holoeye Photonics AG, “LC 2012 Spatial Light Modulator (transmissive)”, Sep. 18, 2013, retrieved from https://web.archive.org/web/20130918151716/http://holoeye.com/spatial-light-modulators/lc-2012-spatial-light-modulator/ on Oct. 20, 2017, 3 pages.
Holoeye Photonics AG, “Spatial Light Modulators”, Oct. 2, 2013, Brochure retrieved from https://web.archive.org/web/20131002061028/http://holoeye.com/wp-content/uploads/Spatial_Light_Modulators.pdf on Oct. 13, 2017, 4 pgs.
Holoeye Photonics AG, “Spatial Light Modulators”, Sep. 18, 2013, retrieved from https://web.archive.org/web/20130918113140/http://holoeye.com/spatial-light-modulators/ on Oct. 13, 2017, 4 pgs.
Horisaki et al., “Irregular Lens Arrangement Design to Improve Imaging Performance of Compound-Eye Imaging Systems”, Applied Physics Express, Jan. 29, 2010, vol. 3, pp. 022501-1-022501-3.
Horisaki et al., “Superposition Imaging for Three-Dimensionally Space-Invariant Point Spread Functions”, Applied Physics Express, Oct. 13, 2011, vol. 4, pp. 112501-1-112501-3.
Horn et al., “LightShop: Interactive Light Field Manipulation and Rendering”, In Proceedings of I3D, Jan. 1, 2007, pp. 121-128.
Isaksen et al., “Dynamically Reparameterized Light Fields”, In Proceedings of SIGGRAPH 2000, pp. 297-306.
Izadi et al., “KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera”, UIST'11, Oct. 16-19, 2011, Santa Barbara, CA, pp. 559-568.
Janoch et al., “A category-level 3-D object dataset: Putting the Kinect to work”, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Nov. 6-13, 2011, Barcelona, Spain, pp. 1168-1174.
Jarabo et al., “Efficient Propagation of Light Field Edits”, In Proceedings of SIACG 2011, pp. 75-80.
Extended European Search Report for European Application No. 10832330.4, completed Sep. 26, 2013, dated Oct. 4, 2013, 7 pgs.
Jiang et al., “Panoramic 3D Reconstruction Using Rotational Stereo Camera with Simple Epipolar Constraints”, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol. 1, Jun. 17-22, 2006, New York, NY, USA, pp. 371-378.
Joshi, Neel S. “Color Calibration for Arrays of Inexpensive Image Sensors”, Master's with Distinction in Research Report, Stanford University, Department of Computer Science, Mar. 2004, 30 pgs.
Joshi et al., “Synthetic Aperture Tracking: Tracking Through Occlusions”, ICCV IEEE 11th International Conference on Computer Vision; Publication [online]. Oct. 2007 [retrieved Jul. 28, 2014]. Retrieved from the Internet: <URL: http:l/ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4409032&isnumber= 4408819>; pp. 1-8.
Kang et al., “Handling Occlusions in Dense Multi-view Stereo”, Computer Vision and Pattern Recognition, 2001, vol. 1, pp. I-103-I-110.
Kim, “Scene Reconstruction from a Light Field”, Master Thesis, Sep. 1, 2010 (Sep. 1, 2010), pp. 1-72.
Kim et al., “Scene reconstruction from high spatio-angular resolution light fields”, ACM Transactions on Graphics (TOG)—SIGGRAPH 2013 Conference Proceedings, vol. 32, Issue 4, Article 73, Jul. 21, 2013, 11 pages.
Kitamura et al., “Reconstruction of a high-resolution image on a compound-eye image-capturing system”, Applied Optics, Mar. 10, 2004, vol. 43, No. 8, pp. 1719-1727.
Konolige, Kurt “Projected Texture Stereo”, 2010 IEEE International Conference on Robotics and Automation, May 3-7, 2010, p. 148-155.
Krishnamurthy et al., “Compression and Transmission of Depth Maps for Image-Based Rendering”, Image Processing, 2001, pp. 828-831.
Kubota et al., “Reconstructing Dense Light Field From Array of Multifocus Images for Novel View Synthesis”, IEEE Transactions on Image Processing, vol. 16, No. 1, Jan. 2007, pp. 269-279.
Kutulakos et al., “Occluding Contour Detection Using Affine Invariants and Purposive Viewpoint Control”, Computer Vision and Pattern Recognition, Proceedings CVPR 94, Seattle, Washington, Jun. 21-23, 1994, 8 pgs.
Lai et al., “A Large-Scale Hierarchical Multi-View RGB-D Object Dataset”, Proceedings—IEEE International Conference on Robotics and Automation, Conference Date May 9-13, 2011, 8 pgs., DOI:10.1109/ICRA.201135980382.
Lane et al., “A Survey of Mobile Phone Sensing”, IEEE Communications Magazine, vol. 48, Issue 9, Sep. 2010, pp. 140-150.
Lee et al., “Automatic Upright Adjustment of Photographs”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 877-884.
Lee et al., “Electroactive Polymer Actuator for Lens-Drive Unit in Auto-Focus Compact Camera Module”, ETRI Journal, vol. 31, No. 6, Dec. 2009, pp. 695-702.
Lee et al., “Nonlocal matting”, CVPR 2011, Jun. 20-25, 2011, pp. 2193-2200.
Lensvector, “How LensVector Autofocus Works”, 2010, printed Nov. 2, 2012 from http://www.lensvector.com/overview.html, 1 pg.
Levin et al., “A Closed Form Solution to Natural Image Matting”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006, vol. 1, pp. 61-68.
Levin et al., “Spectral Matting”, 2007 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 17-22, 2007, Minneapolis, MN, USA, pp. 1-8.
Levoy, “Light Fields and Computational Imaging”, IEEE Computer Society, Sep. 1, 2006, vol. 39, Issue No. 8, pp. 46-55.
Levoy et al., “Light Field Rendering”, Proc. ADM SIGGRAPH '96, pp. 1-12.
Li et al., “A Hybrid Camera for Motion Deblurring and Depth Map Super-Resolution”, Jun. 23-28, 2008, IEEE Conference on Computer Vision and Pattern Recognition, 8 pgs. Retrieved from www.eecis.udel.edu/˜jye/lab_research/08/deblur-feng.pdf on Feb. 5, 2014.
Li et al., “Fusing Images With Different Focuses Using Support Vector Machines”, IEEE Transactions on Neural Networks, vol. 15, No. 6, Nov. 8, 2004, pp. 1555-1561.
Lim, Jongwoo “Optimized Projection Pattern Supplementing Stereo Systems”, 2009 IEEE International Conference on Robotics and Automation, May 12-17, 2009, pp. 2823-2829.
Liu et al., “Virtual View Reconstruction Using Temporal Information”, 2012 IEEE International Conference on Multimedia and Expo, 2012, pp. 115-120.
Lo et al., “Stereoscopic 3D Copy & Paste”, ACM Transactions on Graphics, vol. 29, No. 6, Article 147, Dec. 2010, pp. 147:1-147:10.
Martinez et al., “Simple Telemedicine for Developing Regions: Camera Phones and Paper-Based Microfluidic Devices for Real-Time, Off-Site Diagnosis”, Analytical Chemistry (American Chemical Society), vol. 80, No. 10, May 15, 2008, pp. 3699-3707.
McGuire et al., “Defocus video matting”, ACM Transactions on Graphics (TOG)—Proceedings of ACM SIGGRAPH 2005, vol. 24, Issue 3, Jul. 2005, pp. 567-576.
Merkle et al., “Adaptation and optimization of coding algorithms for mobile 3DTV”, Mobile3DTV Project No. 216503, Nov. 2008, 55 pgs.
Mitra et al., “Light Field Denoising, Light Field Superresolution and Stereo Camera Based Refocussing using a GMM Light Field Patch Prior”, Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on Jun. 16-21, 2012, pp. 22-28.
Moreno-Noguer et al., “Active Refocusing of Images and Videos”, ACM Transactions on Graphics (TOG)—Proceedings of ACM SIGGRAPH 2007, vol. 26, Issue 3, Jul. 2007, 10 pages.
Muehlebach, “Camera Auto Exposure Control for VSLAM Applications”, Studies on Mechatronics, Swiss Federal Institute of Technology Zurich, Autumn Term 2010 course, 67 pgs.
Nayar, “Computational Cameras: Redefining the Image”, IEEE Computer Society, Aug. 14, 2006, pp. 30-38.
Ng, “Digital Light Field Photography”, Thesis, Jul. 2006, 203 pgs.
Ng et al., “Light Field Photography with a Hand-held Plenoptic Camera”, Stanford Tech Report CTSR Feb. 2005, Apr. 20, 2005, pp. 1-11.
Ng et al., “Super-Resolution Image Restoration from Blurred Low-Resolution Images”, Journal of Mathematical Imaging and Vision, 2005, vol. 23, pp. 367-378.
Nguyen et al., “Error Analysis for Image-Based Rendering with Depth Information”, IEEE Transactions on Image Processing, vol. 18, Issue 4, Apr. 2009, pp. 703-716.
Nguyen et al., “Image-Based Rendering with Depth Information Using the Propagation Algorithm”, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 5, Mar. 23-23, 2005, pp. II-589-II-592.
Nishihara, H.K. “PRISM: A Practical Real-Time Imaging Stereo Matcher”, Massachusetts Institute of Technology, A.I. Memo 780, May 1984, 32 pgs.
Nitta et al., “Image reconstruction for thin observation module by bound optics by using the iterative backprojection method”, Applied Optics, May 1, 2006, vol. 45, No. 13, pp. 2893-2900.
Nomura et al., “Scene Collages and Flexible Camera Arrays”, Proceedings of Eurographics Symposium on Rendering, Jun. 2007, 12 pgs.
Park et al., “Multispectral Imaging Using Multiplexed Illumination”, 2007 IEEE 11th International Conference on Computer Vision, Oct. 14-21, 2007, Rio de Janeiro, Brazil, pp. 1-8.
Park et al., “Super-Resolution Image Reconstruction”, IEEE Signal Processing Magazine, May 2003, pp. 21-36.
Parkkinen et al., “Characteristic Spectra of Munsell Colors”, Journal of the Optical Society of America A, vol. 6, Issue 2, Feb. 1989, pp. 318-322.
Perwass et al., “Single Lens 3D-Camera with Extended Depth-of-Field”, printed from www.raytrix.de, Jan. 22, 2012, 15 pgs.
Pham et al., “Robust Super-Resolution without Regularization”, Journal of Physics: Conference Series 124, Jul. 2008, pp. 1-19.
Philips 3D Solutions, “3D Interface Specifications, White Paper”, Feb. 15, 2008, 2005-2008 Philips Electronics Nederland B.V., Philips 3D Solutions retrieved from www.philips.com/3dsolutions, 29 pgs.
Polight, “Designing Imaging Products Using Reflowable Autofocus Lenses”, printed Nov. 2, 2012 from http://www.polight.no/tunable-polymer-autofocus-lens-html--11.html, 1 pg.
Pouydebasque et al., “Varifocal liquid lenses with integrated actuator, high focusing power and low operating voltage fabricated on 200 mm wafers”, Sensors and Actuators A: Physical, vol. 172, Issue 1, Dec. 2011, pp. 280-286.
Protter et al., “Generalizing the Nonlocal-Means to Super-Resolution Reconstruction”, IEEE Transactions on Image Processing, Dec. 2, 2008, vol. 18, No. 1, pp. 36-51.
Radtke et al., “Laser lithographic fabrication and characterization of a spherical artificial compound eye”, Optics Express, Mar. 19, 2007, vol. 15, No. 6, pp. 3067-3077.
Rajan et al., “Simultaneous Estimation of Super Resolved Scene and Depth Map from Low Resolution Defocused Observations”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 9, Sep. 8, 2003, pp. 1-16.
Rander et al., “Virtualized Reality: Constructing Time-Varying Virtual Worlds From Real World Events”, Proc. of IEEE Visualization '97, Phoenix, Arizona, Oct. 19-24, 1997, pp. 277-283, 552.
Rhemann et al, “Fast Cost-Volume Filtering for Visual Correspondence and Beyond”, IEEE Trans. Pattern Anal. Mach. Intell, 2013, vol. 35, No. 2, pp. 504-511.
International Preliminary Report on Patentability for International Application PCT/US13/62720, Report dated Mar. 31, 2015, dated Apr. 9, 2015, 8 pgs.
International Preliminary Report on Patentability for International Application PCT/US2011/064921, dated Jun. 18, 2013, dated Jun. 27, 2013, 14 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/024987, dated Aug 12, 2014, 13 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/027146, completed Aug. 26, 2014, dated Sep. 4, 2014, 10 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/039155, completed Nov. 4, 2014, dated Nov. 13, 2014, 10 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/046002, dated Dec. 31, 2014, dated Jan. 8, 2015, 6 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/048772, dated Dec. 31, 2014, dated Jan. 8, 2015, 8 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/056502, dated Feb. 24, 2015, dated Mar 5, 2015, 7 pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/069932, dated May 19, 2015, dated May 28, 2015, 12 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/017766, dated Aug. 25, 2015, dated Sep. 3, 2015, 8 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/018084, dated Aug. 25, 2015, dated Sep. 3, 2015, 11 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/018116, dated Sep. 15, 2015, dated Sep. 24, 2015, 12 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/021439, dated Sep. 15, 2015, dated Sep. 24, 2015, 9 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/022118, dated Sep. 8, 2015, dated Sep. 17, 2015, 4 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/022123, dated Sep. 8, 2015, dated Sep. 17, 2015, 4 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/022774, dated Sep. 22, 2015, dated Oct. 1, 2015, 5 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/023762, dated Mar. 2, 2015, dated Mar. 9, 2015, 10 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/024407, dated Sep. 15, 2015, dated Sep. 24, 2015, 8 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/024903, dated Sep. 15, 2015, dated Sep. 24, 2015, 12 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/024947, dated Sep. 15, 2015, dated Sep. 24, 2015, 7 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/025100, dated Sep. 15, 2015, dated Sep. 24, 2015, 4 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/025904, dated Sep. 15, 2015, dated Sep. 24, 2015, 5 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/028447, dated Sep. 15, 2015, dated Sep. 24, 2015, 7 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/029052, dated Sep. 15, 2015, dated Sep. 24, 2015, 9 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/030692, dated Sep. 15, 2015, dated Sep. 24, 2015, 6 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/064693, dated May 10, 2016, dated May 19, 2016, 14 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/066229, dated May 24, 2016, dated Jun. 2, 2016, 9 pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/067740, dated May 31, 2016, dated Jun. 9, 2016, 9 pgs.
International Preliminary Report on Patentability for International Application PCT/US2015/019529, dated Sep. 13, 2016, dated Sep. 22, 2016, 9 pgs.
International Preliminary Report on Patentability for International Application PCT/US2015/053013, dated Apr. 4, 2017, dated Apr. 13, 2017, 9 pgs.
International Search Report and Written Opinion for International Application No. PCT/US13/46002, completed Nov. 13, 2013, dated Nov. 29, 2013, 7 pgs.
International Search Report and Written Opinion for International Application No. PCT/US13/56065, Completed Nov. 25, 2013, dated Nov. 26, 2013, 8 pgs.
International Search Report and Written Opinion for International Application No. PCT/US13/59991, Completed Feb. 6, 2014, dated Feb. 26, 2014, 8 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2012/056166, Report Completed Nov. 10, 2012, dated Nov. 20, 2012, 9 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2013/024987, Completed Mar. 27, 2013, dated Apr. 15, 2013, 14 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2013/027146, completed Apr. 2, 2013, 11 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2013/039155, completed Jul. 1, 2013, dated Jul. 11, 2013, 11 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2013/048772, Completed Oct. 21, 2013, dated Nov. 8, 2013, 6 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2013/056502, Completed Feb. 18, 2014, dated Mar. 19, 2014, 7 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2013/069932, Completed Mar 14, 2014, dated Apr. 14, 2014, 12 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2015/019529, completed May 5, 2015, dated Jun. 8, 2015, 11 pgs.
International Search Report and Written Opinion for International Application No. PCT/US2015/053013, completed Dec. 1, 2015, dated Dec. 30, 2015, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US11/36349, dated Aug. 22, 2011, 11 pgs.
International Search Report and Written Opinion for International Application PCT/US13/62720, completed Mar. 25, 2014, dated Apr. 21, 2014, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US14/17766, completed May 28, 2014, dated Jun. 18, 2014, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US14/18084, completed May 23, 2014, dated Jun. 10, 2014, 12 pgs.
International Search Report and Written Opinion for International Application PCT/US14/18116, completed May 13, 2014, dated Jun. 2, 2014, 12 pgs.
International Search Report and Written Opinion for International Application PCT/US14/21439, completed Jun. 5, 2014, dated Jun. 20, 2014, 10 pgs.
International Search Report and Written Opinion for International Application PCT/US14/22118, completed Jun. 9, 2014, dated Jun. 25, 2014, 5 pgs.
International Search Report and Written Opinion for International Application PCT/US14/22774 report completed Jun. 9, 2014, dated Jul. 14, 2014, 6 pgs.
International Search Report and Written Opinion for International Application PCT/US14/24407, report completed Jun. 11, 2014, dated Jul. 8, 2014, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US14/25100, report completed Jul. 7, 2014, dated Aug. 7, 2014, 5 pgs.
International Search Report and Written Opinion for International Application PCT/US14/25904 report completed Jun. 10, 2014, dated Jul. 10, 2014, 6 pgs.
International Search Report and Written Opinion for International Application PCT/US2009/044687, completed Jan. 5, 2010, dated Jan. 13, 2010, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US2010/057661, completed Mar. 9, 2011, 14 pgs.
Rhemann et al., “A perceptually motivated online benchmark for image matting”, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 20-25, 2009, Miami, FL, USA, pp. 1826-1833.
Robert et al., “Dense Depth Map Reconstruction: A Minimization and Regularization Approach which Preserves Discontinuities”, European Conference on Computer Vision (ECCV), 1996, pp. 439-451.
Robertson et al., “Dynamic Range Improvement Through Multiple Exposures”, In Proc. of the Int. Conf. on Image Processing, 1999, 5 pgs.
Robertson et al., “Estimation-theoretic approach to dynamic range enhancement using multiple exposures”, Journal of Electronic Imaging, Apr. 2003, vol. 12, No. 2, pp. 219-228.
Roy et al., “Non-Uniform Hierarchical Pyramid Stereo for Large Images”, Computer and Robot Vision, 2002, pp. 208-215.
Sauer et al., “Parallel Computation of Sequential Pixel Updates in Statistical Tomographic Reconstruction”, ICIP 1995 Proceedings of the 1995 International Conference on Image Processing, Date of Conference: Oct. 23-26, 1995, pp. 93-96.
Scharstein et al., “High-Accuracy Stereo Depth Maps Using Structured Light”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), Jun. 2003, vol. 1, pp. 195-202.
Seitz et al., “Plenoptic Image Editing”, International Journal of Computer Vision 48, Conference Date Jan. 7, 1998, 29 pgs., DOI: 10.1109/ICCV.1998.710696 ⋅ Source: DBLP Conference: Computer Vision, Sixth International Conference.
Shotton et al., “Real-time human pose recognition in parts from single depth images”, CVPR 2011, Jun. 20-25, 2011, Colorado Springs, CO, USA, pp. 1297-1304.
Shum et al., “A Review of Image-based Rendering Techniques”, Visual Communications and Image Processing 2000, May 2000, 12 pgs.
Shum et al., “Pop-Up Light Field: An Interactive Image-Based Modeling and Rendering System”, ACM Transactions on Graphics, Apr. 2004, vol. 23, No. 2, pp. 143-162. Retrieved from http://131.107.65.14/en-us/um/people/jiansun/papers/PopupLightField_TOG.pdf on Feb. 5, 2014.
Silberman et al., “Indoor segmentation and support inference from RGBD images”, ECCV'12 Proceedings of the 12th European conference on Computer Vision, vol. Part V, Oct. 7-13, 2012, Florence, Italy, pp. 746-760.
Stober, “Stanford researchers developing 3-D camera with 12,616 lenses”, Stanford Report, Mar. 19, 2008, Retrieved from: http://news.stanford.edu/news/2008/march19/camera-031908.html, 5 pgs.
Stollberg et al., “The Gabor superlens as an alternative wafer-level camera approach inspired by superposition compound eyes of nocturnal insects”, Optics Express, Aug. 31, 2009, vol. 17, No. 18, pp. 15747-15759.
Sun et al., “Image Super-Resolution Using Gradient Profile Prior”, 2008 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 23-28, 2008, 8 pgs.; DOI: 10.1109/CVPR.2008.4587659.
Taguchi et al., “Rendering-Oriented Decoding for a Distributed Multiview Coding System Using a Coset Code”, Hindawi Publishing Corporation, EURASIP Journal on Image and Video Processing, vol. 2009, Article ID 251081, Online: Apr. 22, 2009, 12 pages.
Takeda et al., “Super-resolution Without Explicit Subpixel Motion Estimation”, IEEE Transaction on Image Processing, Sep. 2009, vol. 18, No. 9, pp. 1958-1975.
Tallon et al., “Upsampling and Denoising of Depth Maps Via Joint-Segmentation”, 20th European Signal Processing Conference, Aug. 27-31, 2012, 5 pgs.
Tanida et al., “Color imaging with an integrated compound imaging system”, Optics Express, Sep. 8, 2003, vol. 11, No. 18, pp. 2109-2117.
Tanida et al., “Thin observation module by bound optics (TOMBO): concept and experimental verification”, Applied Optics, Apr. 10, 2001, vol. 40, No. 11, pp. 1806-1813.
Tao et al., “Depth from Combining Defocus and Correspondence Using Light-Field Cameras”, ICCV '13 Proceedings of the 2013 IEEE International Conference on Computer Vision, Dec. 1, 2013, pp. 673-680.
Taylor, “Virtual camera movement: The way of the future?”, American Cinematographer, vol. 77, No. 9, Sep. 1996, pp. 93-100.
Tseng et al., “Automatic 3-D depth recovery from a single urban-scene image”, 2012 Visual Communications and Image Processing, Nov. 27-30, 2012, San Diego, CA, USA, pp. 1-6.
Vaish et al., “Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures”, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol. 2, Jun. 17-22, 2006, pp. 2331-2338.
Vaish et al., “Synthetic Aperture Focusing Using a Shear-Warp Factorization of the Viewing Transform”, IEEE Workshop on A3DISS, CVPR, 2005, 8 pgs.
Vaish et al., “Using Plane + Parallax for Calibrating Dense Camera Arrays”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004, 8 pgs.
Van Der Wal et al., “The Acadia Vision Processor”, Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception, Sep. 13, 2000, Padova, Italy, pp. 31-40.
Veilleux, “CCD Gain Lab: The Theory”, University of Maryland, College Park-Observational Astronomy (ASTR 310), Oct. 19, 2006, pp. 1-5 (online], [retrieved on May 13, 2014]. Retrieved from the Internet <URL: http://www.astro.umd.edu/˜veilleux/ASTR310/fall06/ccd_theory.pdf, 5 pgs.
Venkataraman et al., “PiCam: An Ultra-Thin High Performance Monolithic Camera Array”, ACM Transactions on Graphics (TOG), ACM, US, vol. 32, No. 6, Nov. 1, 2013, pp. 1-13.
Vetro et al., “Coding Approaches for End-To-End 3D TV Systems”, Mitsubishi Electric Research Laboratories, Inc., TR2004-137, Dec. 2004, 6 pgs.
Viola et al., “Robust Real-time Object Detection”, Cambridge Research Laboratory, Technical Report Series, Compaq, CRL 2001/01, Feb. 2001, Printed from: http://www.hpl.hp.com/techreports/Compaq-DEC/CRL-2001-1.pdf, 30 pgs.
Vuong et al., “A New Auto Exposure and Auto White-Balance Algorithm to Detect High Dynamic Range Conditions Using CMOS Technology”, Proceedings of the World Congress on Engineering and Computer Science 2008, WCECS 2008, Oct. 22-24, 2008, 5 pgs.
Wang, “Calculation Image Position, Size and Orientation Using First Order Properties”, Dec. 29, 2010, OPTI521 Tutorial, 10 pgs.
Wang et al., “Automatic Natural Video Matting with Depth”, 15th Pacific Conference on Computer Graphics and Applications, PG '07, Oct. 29-Nov. 2, 2007, Maui, HI, USA, pp. 469-472.
Wang et al., “Image and Video Matting: A Survey”, Foundations and Trends, Computer Graphics and Vision, vol. 3, No. 2, 2007, pp. 91-175.
Wang et al., “Soft scissors: an interactive tool for realtime high quality matting”, ACM Transactions on Graphics (TOG)—Proceedings of ACM SIGGRAPH 2007, vol. 26, Issue 3, Article 9, Jul. 2007, 6 pages, published Aug. 5, 2007.
Wetzstein et al., “Computational Plenoptic Imaging”, Computer Graphics Forum, 2011, vol. 30, No. 8, pp. 2397-2426.
Wheeler et al., “Super-Resolution Image Synthesis Using Projections Onto Convex Sets in the Frequency Domain”, Proc. SPIE, Mar. 11, 2005, vol. 5674, 12 pgs.
Wieringa et al., “Remote Non-invasive Stereoscopic Imaging of Blood Vessels: First In-vivo Results of a New Multispectral Contrast Enhancement Technology”, Annals of Biomedical Engineering, vol. 34, No. 12, Dec. 2006, pp. 1870-1878, Published online Oct. 12, 2006.
Wikipedia, “Polarizing Filter (Photography)”, retrieved from http://en.wikipedia.org/wiki/Polarizing_filter_(photography) on Dec. 12, 2012, last modified on Sep. 26, 2012, 5 pgs.
Wilburn, “High Performance Imaging Using Arrays of Inexpensive Cameras”, Thesis of Bennett Wilburn, Dec. 2004, 128 pgs.
Wilburn et al., “High Performance Imaging Using Large Camera Arrays”, ACM Transactions on Graphics, Jul. 2005, vol. 24, No. 3, pp. 1-12.
Wilburn et al., “High-Speed Videography Using a Dense Camera Array”, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 2, Jun. 27-Jul. 2, 2004, pp. 294-301.
Wilburn et al., “The Light Field Video Camera”, Proceedings of Media Processors 2002, SPIE Electronic Imaging, 2002, 8 pgs.
Wippermann et al., “Design and fabrication of a chirped array of refractive ellipsoidal micro-lenses for an apposition eye camera objective”, Proceedings of SPIE, Optical Design and Engineering II, Oct. 15, 2005, 59622C-1-59622C-11.
Wu et al., “A virtual view synthesis algorithm based on image inpainting”, 2012 Third International Conference on Networking and Distributed Computing, Hangzhou, China, Oct. 21-24, 2012, pp. 153-156.
Xu, “Real-Time Realistic Rendering and High Dynamic Range Image Display and Compression”, Dissertation, School of Computer Science in the College of Engineering and Computer Science at the University of Central Florida, Orlando, Florida, Fall Term 2005, 192 pgs.
Yang et al., “A Real-Time Distributed Light Field Camera”, Eurographics on Rendering (2002), published Jul. 26, 2002, pp. 1-10.
Yang et al., “Superresolution Using Preconditioned Conjugate Gradient Method”, Proceedings of SPIE—The International Society for Optical Engineering, Jul. 2002, 8 pgs.
Yokochi et al., “Extrinsic Camera Parameter Estimation Based-on Feature Tracking and GPS Data”, 2006, Nara Institute of Science and Technology, Graduate School of Information Science, LNCS 3851, pp. 369-378.
Zhang et al., “A Self-Reconfigurable Camera Array”, Eurographics Symposium on Rendering, published Aug. 8, 2004, 12 pgs.
Zhang et al., “Depth estimation, spatially variant image registration, and super-resolution using a multi-lenslet camera”, Proceedings of SPIE, vol. 7705, Apr. 23, 2010, pp. 770505-770505-8, XP055113797 ISSN: 0277-786X, DOI: 10.1117/12.852171.
Zheng et al., “Balloon Motion Estimation Using Two Frames”, Proceedings of the Asilomar Conference on Signals, Systems and Computers, IEEE, Comp. Soc. Press, US, vol. 2 of 2, Nov. 4, 1991, pp. 1057-1061.
Zhu et al., “Fusion of Time-of-Flight Depth and Stereo for High Accuracy Depth Maps”, 2008 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 23-28, 2008, Anchorage, AK, USA, pp. 1-8.
Zomet et al., “Robust Super-Resolution”, IEEE, 2001, pp. 1-6.
International Search Report and Written Opinion for International Application PCT/US2011/064921, completed Feb. 25, 2011, dated Mar. 6, 2012, 17 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/037670, dated Jul. 18, 2012, Completed Jul. 5, 2012, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/044014, completed Oct. 12, 2012, 15 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/056151, completed Nov. 14, 2012, 10 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/058093, Report completed Nov. 15, 2012, 12 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/059813, completed Dec. 17, 2012, 8 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/022123, completed Jun. 9, 2014, dated Jun. 25, 2014, 5 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/023762, Completed May 30, 2014, dated Jul. 3, 2014, 6 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/024903, completed Jun. 12, 2014, dated Jun. 27, 2014, 13 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/024947, Completed Jul. 8, 2014, dated Aug. 5, 2014, 8 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/028447, completed Jun. 30, 2014, dated Jul. 21, 2014, 8 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/029052, completed Jun. 30, 2014, dated Jul. 24, 2014, 10 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/030692, completed Jul. 28, 2014, dated Aug. 27, 2014, 7 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/064693, Completed Mar. 7, 2015, dated Apr. 2, 2015, 15 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/066229, Completed Mar. 6, 2015, dated Mar. 19, 2015, 9 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/067740, Completed Jan. 29, 2015, dated Mar. 3 2015, 10 pgs.
Office Action for U.S. Appl. No. 12/952,106, dated Aug. 16, 2012, 12 pgs.
“Exchangeable image file format for digital still cameras: Exif Version 2.2”, Japan Electronics and Information Technology Industries Association, Prepared by Technical Standardization Committee on AV & IT Storage Systems and Equipment, JEITA CP-3451, Apr. 2002, Retrieved from: http://www.exif.org/Exif2-2.PDF, 154 pgs.
“File Formats Version 6”, Alias Systems, 2004, 40 pgs.
“Light fields and computational photography”, Stanford Computer Graphics Laboratory, Retrieved from: http://graphics.stanford.edu/projects/lightfield/, Earliest publication online: Feb. 10, 1997, 3 pgs.
Aufderheide et al., “A MEMS-based Smart Sensor System for Estimation of Camera Pose for Computer Vision Applications”, Research and Innovation Conference 2011, Jul. 29, 2011, pp. 1-10.
Baker et al., “Limits on Super-Resolution and How to Break Them”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Sep. 2002, vol. 24, No. 9, pp. 1167-1183.
Barron et al., “Intrinsic Scene Properties from a Single RGB-D Image”, 2013 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 23-28, 2013, Portland, OR, USA, 9 pgs.
Bennett et al., “Multispectral Bilateral Video Fusion”, 2007 IEEE Transactions on Image Processing, vol. 16, No. 5, May 2007, published Apr. 16, 2007, pp. 1185-1194.
Bennett et al., “Multispectral Video Fusion”, Computer Graphics (ACM SIGGRAPH Proceedings), Jul. 25, 2006, published Jul. 30, 2006, 1 page.
Bertalmio et al., “Image Inpainting”, Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, 2000, ACM Pres/Addison-Wesley Publishing Co., 8 pgs.
Bertero et al., “Super-resolution in computational imaging”, Micron, Jan. 1, 2003, vol. 34, Issues 6-7, 17 pgs.
Bishop et al., “Full-Resolution Depth Map Estimation from an Aliased Plenoptic Light Field”, ACCV Nov. 8, 2010, Part II, LNCS 6493, pp. 186-200.
Bishop et al., “Light Field Superresolution”, Computational Photography (ICCP), 2009 IEEE International Conference, Conference Date Apr. 16-17, published Jan. 26, 2009, 9 pgs.
Bishop et al., “The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution”, IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2012, vol. 34, No. 5, published Aug. 18, 2011, pp. 972-986.
Borman, “Topics in Multiframe Superresolution Restoration”, Thesis of Sean Borman, Apr. 2004, 282 pgs.
Borman et al, “Image Sequence Processing”, Dekker Encyclopedia of Optical Engineering, Oct. 14, 2002, 81 pgs.
Borman et al., “Block-Matching Sub-Pixel Motion Estimation from Noisy, Under-Sampled Frames—An Empirical Performance Evaluation”, Proc SPIE, Dec. 28, 1998, vol. 3653, 10 pgs.
Borman et al., “Image Resampling and Constraint Formulation for Multi-Frame Super-Resolution Restoration”, Proc. SPIE, published Jul. 1, 2003, vol. 5016, 12 pgs.
Borman et al., “Linear models for multi-frame super-resolution restoration under non-affine registration and spatially varying PSF”, Proc. SPIE, May 21, 2004, vol. 5299, 12 pgs.
Borman et al., “Nonlinear Prediction Methods for Estimation of Clique Weighting Parameters in NonGaussian Image Models”, Proc. SPIE, Sep. 22, 1998, vol. 3459, 9 pgs.
Borman et al., “Simultaneous Multi-Frame MAP Super-Resolution Video Enhancement Using Spatio-Temporal Priors”, Image Processing, 1999, ICIP 99 Proceedings, vol. 3, pp. 469-473.
Borman et al., “Super-Resolution from Image Sequences—A Review”, Circuits & Systems, 1998, pp. 374-378.
Bose et al., “Superresolution and Noise Filtering Using Moving Least Squares”, IEEE Transactions on Image Processing, Aug. 2006, vol. 15, Issue 8, published Jul. 17, 2006, pp. 2239-2248.
Boye et al., “Comparison of Subpixel Image Registration Algorithms”, Proc. of SPIE—IS&T Electronic Imaging, Feb. 3, 2009, vol. 7246, doi: 10.1117/12.810369, pp. 72460X-1-72460X-9.
Bruckner et al., “Artificial compound eye applying hyperacuity”, Optics Express, Dec. 11, 2006, vol. 14, No. 25, pp. 12076-12084.
Bruckner et al., “Driving microoptical imaging systems towards miniature camera applications”, Proc. SPIE, Micro-Optics, May 13, 2010, 11 pgs.
Bruckner et al., “Thin wafer-level camera lenses inspired by insect compound eyes”, Optics Express, Nov. 22, 2010, vol. 18, No. 24, pp. 24379-24394.
Bryan et al., “Perspective Distortion from Interpersonal Distance Is an Visual Cue for Social Judgments of Faces”, PLOS One, vol. 7, Issue 9, Sep. 26, 2012, e45301, doi:10.1371/journal.pone.0045301, 9 pgs.
Capel, “Image Mosaicing and Super-resolution”, Retrieved on Nov. 10, 2012, Retrieved from the Internet at URL:<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.226.2643&rep=rep1 &type=pdf>, 2001, 269 pgs.
Carroll et al., “Image Warps for Artistic Perspective Manipulation”, ACM Transactions on Graphics (TOG), vol. 29, No. 4, Jul. 26, 2010, Article No. 127, 9 pgs.
Chan et al., “Extending the Depth of Field in a Compound-Eye Imaging System with Super-Resolution Reconstruction”, Proceedings—International Conference on Pattern Recognition, Jan. 1, 2006, vol. 3, 4 pgs.
Chan et al., “Investigation of Computational Compound-Eye Imaging System with Super-Resolution Reconstruction”, IEEE, ISASSP, Jun. 19, 2006, pp. 1177-1180.
Chan et al., “Super-resolution reconstruction in a computational compound-eye imaging system”, Multidim Syst Sign Process, published online Feb. 23, 2007, vol. 18, pp. 83-101.
Chen et al., “Image Matting with Local and Nonlocal Smooth Priors”, CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 23, 2013, pp. 1902-1907.
Chen et al., “Interactive deformation of light fields”, Symposium on Interactive 3D Graphics, 2005, 8 pgs.
Chen et al., “KNN matting”, 2012 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 16-21, 2012, Providence, RI, USA, 8 pgs.
Chen et al., “KNN Matting”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Sep. 2013, vol. 35, No. 9, pp. 2175-2188.
Collins et al., “An Active Camera System for Acquiring Multi-View Video”, IEEE 2002 International Conference on Image Processing, Date of Conference: Sep. 22-25, 2002, Rochester, NY, 4 pgs.
Cooper et al., “The perceptual basis of common photographic practice”, Journal of Vision, vol. 12, No. 5, Article 8, May 25, 2012, pp. 1-14.
Extended European Search Report for EP Application No. 11781313.9, Completed Oct. 1, 2013, dated Oct. 8, 2013, 6 pgs.
Extended European Search Report for EP Application No. 13810429.4, Completed Jan. 7, 2016, dated Jan. 15, 2016, 6 pgs.
Extended European Search Report for European Application EP12782935.6, completed Aug. 28, 2014, dated Sep. 4, 2014, 7 pgs.
Extended European Search Report for European Application EP12804266.0, Report Completed Jan. 27, 2015, dated Feb. 3, 2015, 6 pgs.
Extended European Search Report for European Application EP12835041.0, Report Completed Jan. 28, 2015, dated Feb. 4, 2015, 7 pgs.
Extended European Search Report for European Application EP13751714.0, completed Aug. 5, 2015, dated Aug. 18, 2015, 8 pgs.
Extended European Search Report for European Application EP13810229.8, Report Completed Apr. 14, 2016, dated Apr. 21, 2016, 7 pgs.
Extended European Search Report for European Application No. 13830945.5, Search completed Jun. 28, 2016, dated Jul. 7, 2016, 14 pgs.
Extended European Search Report for European Application No. 13841613.6, Search completed Jul. 18, 2016, dated Jul. 26, 2016, 8 pgs.
Extended European Search Report for European Application No. 14763087.5, Search completed Dec. 7, 2016, dated Dec. 19, 2016, 9 pgs.
Extended European Search Report for European Application No. 14860103.2, Search completed Feb. 23, 2017, dated Mar. 3, 2017, 7 pgs.
Extended European Search Report for European Application No. 14865463.5, Search completed May 30, 2017, dated Jun. 8, 2017, 6 pgs.
Extended European Search Report for European Application No. 15847754.7, Search completed Jan. 25, 2018, dated Feb. 9, 2018, 8 pgs.
Extended European Search Report for European Application No. 18151530.5, Completed Mar. 28, 2018, dated Apr. 20, 2018, 11 pgs.
Supplementary European Search Report for EP Application No. 13831768.0, Search completed May 18, 2016, dated May 30, 2016, 13 pgs.
Supplementary European Search Report for European Application 09763194.9, completed Nov. 7, 2011, dated Nov. 29, 2011, 9 pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2009/044687, Completed Jul. 30, 2010, 9 pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2012/056151, Report dated Mar. 25, 2014, 9 pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2012/056166, Report dated Mar. 25, 2014, Report dated Apr. 3, 2014 8 pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2012/058093, Report dated Sep. 18, 2013, dated Oct. 22, 2013, 40 pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2012/059813, Search Completed Apr. 15, 2014, 7 pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2013/059991, dated Mar. 17, 2015, dated Mar. 26, 2015, 8 pgs.
International Preliminary Report on Patentability for International Application PCT/US10/057661, dated May 22, 2012, dated May 31, 2012, 10 pgs.
International Preliminary Report on Patentability for International Application PCT/US11/036349, Report dated Nov. 13, 2012, dated Nov. 22, 2012, 9 pgs.
International Preliminary Report on Patentability for International Application PCT/US13/56065, dated Feb. 24, 2015, dated Mar. 5, 2015, 4 pgs.
Related Publications (1)
Number Date Country
20190037116 A1 Jan 2019 US
Provisional Applications (1)
Number Date Country
61783441 Mar 2013 US
Continuations (2)
Number Date Country
Parent 15438542 Feb 2017 US
Child 16148317 US
Parent 14776553 US
Child 15438542 US