Patent Grant
9214013
The present invention generally relates to the manipulation of images and more specifically the manipulation of light field images.
Binocular viewing of a scene creates two slightly different images of the scene due to the different fields of view of each eye. These differences, referred to as binocular disparity (or parallax), provide information that can be used to calculate depth in the visual scene, providing a major means of depth perception. The impression of depth associated with stereoscopic depth perception can also be obtained under other conditions, such as when an observer views a scene with only one eye while moving. The observed parallax can be utilized to obtain depth information for objects in the scene. Similar principles in machine vision can be used to gather depth information.
Two or more cameras separated by a distance can take pictures of the same scene and the captured images can be compared by shifting the pixels of two or more images to find parts of the images that match. The amount an object shifts between different camera views is called the disparity, which is inversely proportional to the distance to the object. A disparity search that detects the shift of an object in multiple images can be used to calculate the distance to the object based upon the baseline distance between the cameras and the focal length of the cameras involved. The approach of using two or more cameras to generate stereoscopic three-dimensional images is commonly referred to as multi-view stereo.
Multi-view stereo can generally be described in terms of the following components: matching criterion, aggregation method, and winner selection. The matching criterion is used as a means of measuring the similarity of pixels or regions across different images. A typical error measure is the RGB or intensity difference between images (these differences can be squared, or robust measures can be used). Some methods compute subpixel disparities by computing the analytic minimum of the local error surface or use gradient-based techniques. One method involves taking the minimum difference between a pixel in one image and the interpolated intensity function in the other image. The aggregation method refers to the manner in which the error function over the search space is computed or accumulated. The most direct way is to apply search windows of a fixed size over a prescribed disparity space for multiple cameras. Others use adaptive windows, shiftable windows, or multiple masks. Another set of methods accumulates votes in 3D space, e.g., a space sweep approach and voxel coloring and its variants. Once the initial or aggregated matching costs have been computed, a decision is made as to the correct disparity assignment for each pixel. Local methods do this at each pixel independently, typically by picking the disparity with the minimum aggregated value. Cooperative/competitive algorithms can be used to iteratively decide on the best assignments. Dynamic programming can be used for computing depths associated with edge features or general intensity similarity matches. These approaches can take advantage of one-dimensional ordering constraints along the epipolar line to handle depth discontinuities and unmatched regions. Yet another class of methods formulate stereo matching as a global optimization problem, which can be solved by global methods such as simulated annealing and graph cuts.
More recently, researches have used multiple cameras spanning a wider synthetic aperture to capture light field images (e.g. the Stanford Multi-Camera Array). A light field, which is often defined as a 4D function characterizing the light from all direction at all points in a scene, can be interpreted as a two-dimensional (2D) collection of 2D images of a scene. Due to practical constraints, it is typically difficult to simultaneously capture the collection of 2D images of a scene that form a light field. However, the closer in time at which the image data is captured by each of the cameras, the less likely that variations in light intensity (e.g. the otherwise imperceptible flicker of fluorescent lights) or object motion will result in time dependent variations between the captured images. Processes involving capturing and resampling a light field can be utilized to simulate cameras with large apertures. For example, an array of M×N cameras pointing at a scene can simulate the focusing effects of a lens as large as the array. Use of camera arrays in this way can be referred to as synthetic aperture photography.
While stereo matching was originally formulated as the recovery of 3D shape from a pair of images, a light field captured using a camera array can also be used to reconstruct a 3D shape using similar algorithms to those used in stereo matching. The challenge, as more images are added, is that the prevalence of partially occluded regions (pixels visible in some but not all images) also increases.
Systems and methods in accordance with embodiments of the invention enable the correction of user identified artifacts in light field images. One embodiment of the invention is a method for correcting artifacts in a light field image rendered from a light field obtained by capturing a set of images from different viewpoints and initial depth estimates for pixels within the light field using a processor configured by an image processing application, where the method includes: receiving a user input indicating the location of an artifact within said light field image; selecting a region of the light field image containing the indicated artifact; generating updated depth estimates for pixels within the selected region; and re-rendering at least a portion of the light field image using the updated depth estimates for the pixels within the selected region.
In a further embodiment, the rendering of the light field image utilizes initial visibility information with respect to pixels in the light field and the method further includes generating updated visibility information for pixels within the selected region. In addition, re-rendering at least a portion of the light field image further includes using the updated visibility information for the pixels within the selected region.
Another embodiment also includes receiving a user input validating the re-rendering of the at least a portion of the light field image, and updating the light field image, the initial depth estimates, and the initial visibility information.
In a still further embodiment, the set of images includes images captured in a plurality of color channels.
In still another embodiment, the light field image is rendered from the light field and the depth estimates using a super-resolution process.
In a yet further embodiment, receiving a user input indicating the location of an artifact within said light field image includes receiving a user selection of at least one pixel in said light field image.
In yet another embodiment, selecting a region of the light field image containing the indicated artifact further includes identifying additional pixels within the neighborhood of the at least one selected pixel in said light field image.
In a further embodiment again, identifying additional pixels further includes identifying pixels within a predetermined neighborhood surrounding each of the at least one selected pixel in said light field image.
In another embodiment again, identifying additional pixels further includes identifying pixels within a neighborhood surrounding each of the at least one selected pixel in said light field image that adapts based upon the local characteristics of the pixels in the neighborhood of the at least one selected pixel.
In a further additional embodiment, the initial depth estimates include confidence metrics describing the reliability of the initial depth estimates, and identifying additional pixels further includes identifying pixels having depth estimates with associated confidence metrics below a threshold.
In another additional embodiment, generating updated depth estimates for pixels within the selected region further includes receiving a user input identifying of a region of said light field image that does not contain said artifact, and determining an updated depth estimate based upon the initial depth estimates of pixels within the identified region of said light field image.
In a still yet further embodiment, receiving a user input identifying a region of said light field image that does not contain said artifact further includes receiving a user selection of at least one pixel in said light field image.
In still yet another embodiment, receiving a user input identifying a region of said light field image that does not contain said artifact further includes identifying additional pixels within the neighborhood of the at least one selected pixel in said light field image.
In a still further embodiment again, identifying additional pixels further includes identifying pixels within a predetermined neighborhood surrounding each of the at least one selected pixel in said light field image.
In still another embodiment again, identifying additional pixels further includes identifying pixels within a neighborhood surrounding each of the at least one selected pixel in said light field image that adapts based upon the local characteristics of the pixels in the neighborhood of the at least one selected pixel.
In a still further additional embodiment, determining an updated depth estimate based upon the initial depth estimates of pixels within the identified region of said light field image further includes averaging the initial depths of pixels within the identified region that does not contain said artifact, and updating the depth estimates of pixels within the selected region containing said artifact.
In still another additional embodiment, the initial depth estimates include confidence metrics describing the reliability of the initial depth estimates; and averaging the initial depths of pixels within the identified region further includes filtering pixels from the averaging process that have depth estimates with confidence metrics that are below a threshold level of reliability.
In a yet further embodiment again, the confidence metrics include a set of confidence values associated with the depth information assigned to at least one pixel within the light field image; and updating the depth estimates of pixels within the selected region containing said artifact further includes: comparing the confidence value associated with a given pixel within the selected region containing said artifact to a second confidence value associated with at least one pixel in the identified region of the light field image that does not contain said artifact; and when the difference between the first and second confidence values is less than a threshold, updating the depth estimate of the given pixel with said average depth estimate.
In yet another embodiment again, averaging the initial depths of pixels within the identified region further includes filtering pixels from the averaging process that are outliers relative to the other pixels within the identified region that does not contain said artifact.
In a yet further additional embodiment, generating updated depth estimates further includes: receiving a user input indicating a specific depth; and determining updated depth estimates for pixels in the selected region of said light field image containing said artifact based upon said specific depth.
In yet another additional embodiment, determining updated depth estimates for pixels in the selected region of said light field image containing said artifact based upon said specific depth further includes: comparing a confidence value associated with a given pixel within the selected region containing said artifact to a second confidence value associated with the specific depth indicated by the user input; and when the difference between the first and second confidence values is less than a threshold, updating the depth estimate of the given pixel with said specific depth.
In a further additional embodiment again, generating updated depth estimates further includes: iteratively re-rendering a portion of said light field image containing said artifact in response to received user inputs indicating a plurality of potential depths; and determining updated depth estimates for pixels in the selected region of said light field image containing said artifact based upon receipt of a user input validating one of said plurality of potential depths.
In another additional embodiment again, the rendering of the light field image utilizes initial visibility information with respect to pixels in the light field and the method further includes generating updated visibility information for pixels within the selected region by re-rendering a portion of said light field image multiple times by applying different visibility information to the light field, and determining updated visibility information for pixels in the selected region of said light field image containing said artifact based upon receipt of a user input selecting one of said multiple renderings of said portion of said light field image.
In another further embodiment, the rendering of the light field image utilizes initial visibility information with respect to pixels in the light field and the method further includes generating updated depth estimates and visibility information for pixels within the selected region by searching a plurality of combinations of depth and visibilities for pixels within the selected region of said light field image containing said artifact, and selecting one of said plurality of combinations of depth and visibilities based upon the selection resulting in the best overall match between the pixels in the selected region of said light field image and corresponding pixels within the light field, where the corresponding pixels within the light field are identified based upon the selected depth and visibilities.
In still another further embodiment, re-rendering at least a portion of the light field image using the updated depth estimates for the pixels within the selected region further includes using a super-resolution process to re-render said at least a portion of the light field image.
In yet another further embodiment, re-rendering at least a portion of the light field image using the updated depth estimates for the pixels within the selected region further includes shifting pixels within the selected region along epipolar lines based upon the updated depth estimates.
An image processing system in accordance with an embodiment of the invention includes: a processor; and memory (or other form of non-transitory machine readable media) containing an image processing application. In addition, the image processing application configures the processor to: receive a user input indicating the location of an artifact within said light field image rendered from a light field obtained by capturing a set of images from different viewpoints and initial depth estimates for pixels within the light field; select a region of the light field image containing an indicated artifact; generate updated depth estimates for pixels within a selected region; and re-rendering at least a portion of a light field image using the updated depth estimates for the pixels within the selected region.
A further embodiment also includes an array camera module. In addition, the image processing application configures the processor to: capture a set of images from different viewpoints; generate initial depth estimates based upon the disparity between corresponding pixels in a captured set of images; render a light field image using a super-resolution process based upon a set of images and initial depth estimates; and store a rendered light field image in said memory.
Turning now to the drawings, systems and methods for correcting user identified artifacts in light field images in accordance with embodiments of the invention are illustrated. Array cameras, such as those described in U.S. patent application Ser. No. 12/935,504 entitled “Capturing and Processing of Images using Monolithic Camera Array with Heterogeneous Imagers” to Venkataraman et al., can be utilized to capture light field images. In a number of embodiments, super-resolution processes such as those described in U.S. patent application Ser. No. 12/967,807 entitled “Systems and Methods for Synthesizing High Resolution Images Using Super-Resolution Processes” to Lelescu et al., are utilized to synthesize a higher resolution 2D image or a stereo pair of higher resolution 2D images from the lower resolution images in the light field captured by an array camera. The terms high or higher resolution and low or lower resolution are used here in a relative sense and not to indicate the specific resolutions of the images captured by the array camera. The disclosures of U.S. patent application Ser. No. 12/935,504 and U.S. patent application Ser. No. 12/967,807 are hereby incorporated by reference in their entirety.
Each two-dimensional (2D) image in a captured light field is from the viewpoint of one of the cameras in the array camera. Due to the different viewpoint of each of the cameras, parallax results in variations in the position of objects within the different images of the scene. Techniques for determining pixel disparity as a result of parallax between the different cameras in the array, so that appropriate scene-dependent geometric shifts can be applied to the pixels of the captured images when performing super-resolution processing are described in U.S. patent application Ser. No. 13/972,881 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”, filed Aug. 21, 2013. The disclosure of U.S. patent application Ser. No. 13/972,881 is hereby incorporated by reference in its entirety.
A high resolution image synthesized using super-resolution processing is synthesized from a specific viewpoint that can be referred to as a reference viewpoint. The reference viewpoint can be from the viewpoint of one of the cameras in a camera array. Many array cameras capture color information using different cameras (see for example the array cameras disclosed in U.S. patent application Ser. No. 12/935,504). In many embodiments, the viewpoint of a Green camera is utilized as the reference viewpoint. In several embodiments, the array camera can include one or more cameras that capture image data in multiple color channels. For example, an array camera may include at least one camera that has a Bayer color filter pattern and that is used as a reference camera. Alternatively, the reference viewpoint can be an arbitrary virtual viewpoint where there is no physical camera. A benefit of synthesizing a high resolution image from the viewpoint of one of the cameras (as opposed to a virtual viewpoint) is that the disparity of the pixels in the light field can be determined with respect to the image in the light field captured from the reference viewpoint. When a virtual viewpoint is utilized, none of the captured image data is from the reference viewpoint and so the process instead relies solely on cameras away from the reference viewpoint to determine the best match.
Array cameras in accordance with many embodiments of the invention use the disparity between the pixels in the images within a light field to generate a depth map from the reference viewpoint. A depth map indicates the distance 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 a captured light field to correct for disparity when performing super-resolution processing. In many embodiments, the depth map is expressed in terms of depth estimates for individual pixels or groups of pixels within a light field image synthesized from a reference viewpoint. Depth estimates can also be determined for pixels that are occluded in the reference viewpoint. In several embodiments, an initial depth map of the reference viewpoint is generated and as part of that process, or as a subsequent process, occluded pixels and/or other types of mismatched pixels are detected. The process of detecting pixels that are occluded can also be thought of as determining whether a pixel in an image captured from the reference viewpoint is visible in the image from a non-reference viewpoint. When a pixel in the image captured from the reference viewpoint is not visible in a second image, utilizing image data from the second image when determining the depth of the pixel in the reference image introduces error into the depth determination. Therefore, by detecting the pixels in the reference image that are occluded in one or more images in the light field, the accuracy of the depth map can be improved.
A depth map from a reference viewpoint can be utilized to determine the scene dependent geometric shifts that are likely to have occurred in images captured from other viewpoints. These scene dependent geometric shifts can be utilized in super-resolution processing. In addition, the scene dependent geometric shifts can be utilized to refine the determinations of the visibility of pixels within the light field from the reference viewpoint. In several embodiments, visibility information is generated and provided along with the depth map for use in super-resolution processing.
Once a depth map and visibility information are generated for the pixels in the light field, the depth map and visibility information can be provided to a super-resolution processing pipeline to synthesize a higher resolution 2D image of the scene. This process can also be referred to as rendering a light field image. The depth map can be utilized to correct for parallax between the different low resolution images and visibility information can be utilized during fusion to avoid the fusion of occluded pixels (i.e. pixels in an alternate view image that are not visible from the reference viewpoint). In several embodiments, the process of generating a depth map also includes generating a confidence map that includes confidence metrics for the depth estimates in the depth map. In many embodiments, the confidence metrics encode at least one confidence factor indicative of the reliability of the corresponding depth estimate.
Where a parallax detection process results in errors in the depth estimates and/or the visibility information, the errors can translate into artifacts in a rendered light field image. These artifacts may be apparent in any color channel and are a function of the fact that the super-resolution processes used to render the light field images rely upon the assumption of accurate image registration, which includes the elimination of disparity due to parallax and the discarding of pixels that are not visible in the reference viewpoint. In several embodiments, a user interface is provided that enables a user to identify artifacts in light field images and provide feedback that assists in the determination of the correct depth and/or visibility of the pixels within the captured light fields that are responsible for the identified artifacts. Corrected depth information can be provided to an image processing application configured to perform a super-resolution process to re-render the light field image using the updated depth and/or visibility information with the goal of reducing and/or eliminating the artifact(s) identified by the user. Although much of the discussion that follows references the use of super-resolution processing to re-render light field images and/or portions of light field images, it should be appreciated that light field images can be re-rendered from appropriately formatted light field image data, such as but not limited to light field image data stored in the manner outlined in U.S. patent application Ser. No. 13/631,736 entitled “Systems and Methods for Decoding Light Field Image Files”, filed Sep. 28, 2012 by shifting pixel locations based upon updated depth and/or visibility information, and revealing otherwise occluded pixels. Accordingly, the term rendering and/or re-rendering should not be understood as necessarily limited to the use of a super-resolution process. The disclosure of U.S. patent application Ser. No. 13/631,736 is hereby incorporated by reference in its entirety.
Although the invention is described with respect to light field images having depth information, in many embodiments any image having depth information may be utilized. Systems and methods for identifying and correcting depth related artifacts in rendered light field images in accordance with embodiments of the invention are discussed below.
System Overview
Users can utilize a variety of devices to view and interact with light field images in accordance with embodiments of the invention. A network diagram illustrating variety of devices that users can utilize to view, interact and share light field images in accordance with an embodiment of the invention is illustrated in
Devices in accordance with embodiments of the invention can utilize any of a variety of user interfaces to interact with light field images. In a number of embodiments, devices capable of manipulating light field images employ touchscreen-based interfaces. By utilizing touchscreen-based interfaces, users can select regions in a light field image in order to identify any depth related artifacts, and can provide correct depth information. In many embodiments, the correct depth information can be provided in a variety of ways including (but not limited to) provided by indicating the correct boundaries of areas and/or regions within the image that contain pixels with which the correct depth is associated. User guided boundary selection can be utilized to perform segmentation of foreground objects from background objects.
Although specific devices are described above with respect to
Array Cameras
Array cameras in accordance with many embodiments of the invention can include a camera module including an array of cameras and a processor configured to read out and process image data from the camera module to synthesize light field images. An array camera in accordance with an embodiment of the invention is illustrated in
Processors 108 in accordance with many embodiments of the invention are configured using appropriate software to take the image data within the light field and synthesize one or more high resolution images. In several embodiments, the high resolution image is synthesized from a reference viewpoint, typically that of a reference focal plane 204 within the sensor 202. In many embodiments, the processor is able to synthesize an image from a virtual viewpoint, which does not correspond to the viewpoints of any of the focal planes 204 in the sensor 202. The images in the light field will include a scene-dependent disparity due to the different fields of view of the focal planes used to capture the images.
In the illustrated embodiment, the array camera includes a display capable of displaying light field images synthesized by the processor. In several embodiments, the display 212 is capable of displaying 3D images. In a number of embodiments, the processor 208 is capable of receiving user input via any of a variety of user input mechanism including (but not limited to) a touchscreen interface, a pointing device, and/or a keyboard. In several embodiments, the user input can be received via a camera or array camera capable of tracking user movements. In a number of embodiments, the user input can be received via a microphone capable of detecting audio inputs. In many embodiments, a user interface provided by the device enables a user to identify artifacts in light field images and provide feedback that assists in the determination of the correct depth and/or visibility of the pixels responsible for the identified artifacts. The corrected depth and/or visibility information can then be utilized to resynthesize a high resolution image with a goal of reducing and/or eliminating the impact of the identified artifact on the resulting image.
In a number of embodiments, the processor 208 is connected to a network interface (not shown) that enables the array camera to share light field images via a network. Although a specific array camera architecture is illustrated in
Array Camera Modules
Array camera modules in accordance with several embodiments of the invention can be constructed from an imager array or sensor including an array of focal planes and an optic array including a lens stack for each focal plane in the imager array. Sensors including multiple focal planes are discussed 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. Light filters can be used within each optical channel formed by the lens stacks in the optic array to enable different cameras within an array camera module to capture image data with respect to different portions of the electromagnetic spectrum (i.e. within different spectral channels).
An array camera module in accordance with an embodiment of the invention is illustrated in
In the illustrated embodiment, the focal planes are configured in a 5×5 array. Each focal plane 154 on the sensor is capable of capturing an image of the scene. Typically, each focal plane includes a plurality of rows of pixels that also forms a plurality of columns of pixels, and each focal plane is contained within a region of the imager that does not contain pixels from another focal plane. In many embodiments, image data capture and readout of each focal plane can be independently controlled. In this way, image capture settings including (but not limited to) the exposure times and analog gains of pixels within a focal plane can be determined independently to enable image capture settings to be tailored based upon factors including (but not limited to) a specific color channel and/or a specific portion of the scene dynamic range. The sensor elements utilized in the focal planes can be individual light sensing elements such as, but not limited to, traditional CIS (CMOS Image Sensor) pixels, CCD (charge-coupled device) pixels, high dynamic range sensor elements, multispectral sensor elements and/or any other structure configured to generate an electrical signal indicative of light incident on the structure. In many embodiments, the sensor elements of each focal plane have similar physical properties and receive light via the same optical channel and color filter (where present). In other embodiments, the sensor elements have different characteristics and, in many instances, the characteristics of the sensor elements are related to the color filter applied to each sensor element.
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 Ser. No. 13/875,248 entitled “Camera Modules Patterned with pi FilterGroups” filed May 1, 2013, the disclosure of which is incorporated by reference herein in its entirety. Any of a variety of color filter configurations can be utilized including the configuration in
Although specific array cameras and imager arrays are discussed above, many different array cameras can be utilized to capture image data and synthesize images in accordance with embodiments of the invention. Systems and methods for correcting user identified artifacts in light field images generated from pixels in light fields captured by array cameras in accordance with embodiments of the invention are discussed below.
Interactive Artifact Correction of Regions in Light Field Images
Interactive artifact correction allows for the correction of depth related artifacts by modifying the depth estimates and/or visibility information associated with pixels in regions of a light field image and re-rendering the light field image in real time based upon user input until the artifacts are corrected. In many embodiments, the locations of artifacts within a light field image are identified based upon user input. User input can also be utilized to provide guidance concerning the appropriate depth and/or visibility of pixels within the region containing the artifact. In several embodiments, users provide depth information by indicating another region of the image that captures objects located at a similar depth. In a number of embodiments, the process of indicating depth is interactive. In certain embodiments, a depth slider is provided and the user can modify the depth slider to observe whether changing the depth estimate reduces the appearance of an artifact contained within an image portion that is re-rendered in real time in response to the user input. Similarly, various image portions rendered using different combinations of likely depth estimates and/or visibility patterns can be presented to the user and the user can select the image portion in which the artifact is least noticeable.
A process for interactive artifact correction of regions in a light field image that contain artifacts in accordance with an embodiment of the invention is illustrated in
In several embodiments, the locations of artifacts may be determined (310) manually and/or via an automated process. In a number of embodiments, the location of artifacts is determined (310) in response to user inputs such as (but not limited to) pointing and/or clicking on the location of an artifact utilizing a touch interface, a mouse, trackpad, or other pointing device. In many embodiments, the location of an artifact is determined (310) based upon a user looking at an artifact and having the artifact selected utilizing eye tracking techniques to identify an image location from a real time stream of video in which the user's eyes are visible. In several embodiments, the location of an artifact can be determined (310) automatically utilizing a classifier configured to detect artifacts. Various techniques for enabling a user to provide input identifying the location of a region containing an artifact in accordance with embodiments of the invention are discussed below with reference to
In many embodiments, the location of artifacts can be determined (310) using a confidence map that describes the reliability of depth estimates utilized to synthesize the initial light field image. Confidence maps can be generated at the time of depth map computation using a variety of methods including those techniques disclosed in U.S. patent application Ser. No. 13/972,881, which is incorporated by reference above. A confidence map typically includes confidence metrics for the depth estimates in a depth map. In several embodiments, the confidence metrics encode at least one confidence factor indicative of the reliability of the corresponding depth estimate. In a number of embodiments, the confidence metric includes at least a confidence factor based on the signal to noise ratio (SNR) in the region of the pixel location with which the depth estimate is associated, and a confidence factor based upon the number of pixels in a set of images that correspond to the pixel location with which the depth map is associated that were utilized to generate the depth estimate and/or are occluded.
An area around an artifact can be selected and the depth estimates and/or visibility information of the image data utilized to synthesize the area around the artifact can be modified and the area re-rendered in an attempt to reduce the impact and/or eliminate the presence of the artifact. The selected (312) area surrounding the location of an artifact may have a variety of shapes as appropriate to the requirements of specific applications in accordance with embodiments of the invention. Shapes for selected (312) areas include, but are not limited to, rectangular shapes, fixed non-rectangular shapes such as circles and triangles, and adaptive shapes based upon the determined (310) artifacts. In several embodiments, adaptive shapes are predetermined. Predetermined adaptive shapes include, but are not limited to, masks and shiftable windows. In a number of embodiments, adaptive shapes utilize segmentation techniques to dynamically bound the determined (310) locations of artifacts. Segmentation techniques in accordance with embodiments of the invention utilize one or more aspects of the light field image, including, but not limited to, intensity, color, texture, a confidence map, depth information, and bilateral support as appropriate to the requirements of specific applications. In accordance with numerous embodiments of the invention, the selected (312) area may match the determined (310) location of an artifact and/or may contain padding around the determined (310) location of the artifact. The amount of padding may be predetermined and/or determined dynamically.
Determining (314) the correct depth and/or visibility information for the image data used to synthesize the selected (312) area of the light field image may be performed via user input and/or automatically as appropriate to the requirements of specific applications in accordance with embodiments of the invention. In several embodiments, the correct depth is determined (314) utilizing input received using an input device, such as (but not limited to) a touchscreen interface. In many embodiments, the user can indicate a depth by selecting an area (e.g. a region) within the light field image that has the same depth as the pixels responsible for the selected artifact. In a number of embodiments, the received input is restricted to pixels that have depth estimates with a corresponding high confidence value in a confidence map. In certain embodiments, a depth slider is provided and the user can select a depth as the area of the image is re-rendered in real time. Similarly, the image can be re-rendered using different depth estimates and/or visibility patterns and the user can select the combination that results in the greatest reduction in the artifact. As can be readily appreciated, any of a number of techniques can be utilized to receive depth and/or visibility inputs from users as appropriate to the requirements of specific applications in accordance with embodiments of the invention. Various techniques for receiving user input concerning depth and/or visibility information in accordance with embodiments of the invention are described below with reference to
In a number of embodiments, the determined (314) depth is a depth sampled from a region of the light field image that is not contained within the selected (312) area. In many embodiments, the determined (314) depth corresponds to the depth of one or more pixels in the selected (312) area. In several embodiments, the depth may be determined (314) utilizing filtering techniques, including, but not limited to, box filters, adaptive filters, and edge-preserving filters. One filter which may be utilized in accordance with embodiments of the invention is a joint bilateral filter modulated by a mask of 1 for high confidence values and 0 for low confidence values. In many embodiments, the determined (314) depth is selected from a predetermined set of depths. The predetermined set of depths can be, but are not limited to, the entire range of depths supported by an array camera, restricted to a number of depths which are based on the selected (312) area, depths with an associated confidence value above a threshold value, and/or those depths that are greater than the depth of the object in whose occlusion map the current set of pixels are located. As can be readily appreciated, any of a number of techniques can be utilized to search different depth estimates and/or visibility patterns in order to reduce the impact of an artifact within an area of a light field image as appropriate to the requirements of specific applications in accordance with embodiments of the invention. Various techniques for automatically selecting alternative depth estimates and/or visibility patterns to utilize in re-rendering an area of a light field image containing an artifact identified by a user in accordance with embodiments of the invention are discussed below with reference to
In many embodiments, assigning (316) the updated depth to the pixels from the light field data utilized to synthesize a selected (312) area of a light field image is performed by assigning (316) the depth information of one or more pixels in the selected (312) area. In several embodiments, assigning (316) depth to one or more pixels in the selected (312) area utilizes a cost function or a confidence value associated with the depth of the pixels, where the cost or confidence value of the current depth of a pixel is compared to the cost or confidence value of the depth to be assigned to that pixel. In the event that the difference in costs or confidence exceeds a threshold, then the depth estimate for the pixel remains unchanged. When the difference is less than the threshold, then the new depth estimate is utilized with respect to the pixel in the re-rendering of the selected area. In several embodiments, the confidence of the original depth estimate can be utilized in determining the threshold and/or whether to modify the depth estimate for the pixel. A variety of cost functions can be utilized as appropriate to the requirements of specific applications in accordance with embodiments of the invention including (but not limited to) those disclosed in U.S. patent application Ser. No. 13/972,881, which is incorporated by reference above.
In several embodiments, the entire light field image is re-rendered (318) using depth and/or visibility information including the updated depth and/or visibility assigned (316) to the pixels within the selected area of the light field image containing the artifact. In a number of embodiments, only portions of the light field image containing pixels having depths corresponding to the assigned (316) depth are re-rendered. The corresponding portions can include the selected (312) area only and/or areas surrounding the selected (312) area.
The re-rendered (318) light field image or a portion of the light field image that is re-rendered can be validated (320) utilizing a variety of techniques in accordance with embodiments of the invention. In a number of embodiments, validation (320) is performed in real time using input received via an input device. In many embodiments, validation (320) is performed by selecting between multiple versions of a portion of a light field image re-rendered using different depth and/or visibility information. In several embodiments, validation (320) is performed using an automated process, such as (but not limited to) any of the automated processes described above for determining (310) the presence and/or location of artifacts within a light field image.
Although specific processes are described above with reference to
Correcting User Identified Artifacts
The selection of an artifact by a user enables the correction of the artifact by modifying the depth and/or visibility information of the pixels within the light field that are responsible for the artifact in the rendered light field image. A variety of processes can be utilized to determine the best depth and/or visibility information to utilize, including processes that rely upon user input and processes that are completely automated. A generalized process for correcting user identified artifacts in light field images in accordance with an embodiment of the invention is illustrated in
Based upon one or more candidate depth estimates and/or visibility patterns, at least one image portion is re-rendered (414) by the super-resolution process. In several embodiments, the depth estimate and/or visibility pattern that results in the greatest reduction in the identified artifact can be selected (416) based upon user input. In addition, the selection can be iterative with the user selecting different depth and/or visibility patterns, observing the re-rendered image portion in real time, and then trying different alternatives until a final image portion is selected. When a final image portion is selected, the light field image is updated (418) in a process that can involve updating the intensity information of the pixels in the rendered light field image, the depth estimates in the depth map, and/or the confidence metrics in the confidence map. In several instances, user validation can be interpreted as a depth estimate determined with a high degree of confidence and this information can be encoded in the confidence metrics for the impacted pixels. The process of updating the light field image can involve re-rendering a portion of the light field image. The re-rendered portion of the light field image can be larger than the selected area containing the artifact so as to avoid the creation of additional artifacts as a result of the re-rendering process.
Although specific processes are described above with reference to
Identifying Artifacts and Selecting Regions for Processing
An artifact is typically irregularly shaped and the process of correcting an artifact typically involves shifting the pixels creating the artifacts along epipolar lines (defined based upon the cameras that captured the image data used to synthesize the light field image). Therefore, assigning correct depth and visibility estimates to pixels associated with an artifact can impact regions of the image that do not contain the artifact when the light field image is re-rendered. Assigning the correct depth information to a pixel is intended to shift the pixel from a location in the artifact to its correct location somewhere else in the image or remove it from the image, where a pixel in the light field is not actually visible form the reference viewpoint. In many embodiments, a region in the neighborhood of one or more artifacts is re-rendered so that the re-rendering updates the portions of the image containing the artifact and the portions of the image to which pixels are shifted following the updating of their depth and/or visibility information.
A process for selecting a region containing a user identified artifact in accordance with an embodiment of the invention is illustrated in
To refine the locations of individual pixels, which can be especially useful for inaccurate pointing methods, and to assist with the artifact correction, additional pixels in the neighborhood of the selected pixel(s) are identified (512). The size of the neighborhood may be predetermined. In several embodiments, the neighborhood is square (N×N), rectangular (M×N), and/or has an arbitrary shape within a window. In various embodiments, the size of the neighborhood is fixed (generally as a small window such as, but not limited to, 3×3 or 5×5 pixels). In several embodiments, the size of the neighborhood is a single pixel.
In a number of embodiments, the neighborhood size is adaptive based on the local content around the pixels identified (510) by the user. In certain embodiments, the window is defined to be adaptive using a variety of techniques appropriate to the requirements of specific applications including (but not limited) to defining a window based upon a bilateral support in which the degree of pixel similarity is used to determine the shape of the neighborhood. In many embodiments, the size of the neighborhood can be determined based upon the (accuracy of) the pointing device or input modality used to identify (510) the pixels that form part of the artifact. In some embodiments, when the individual pixels selected (510) by the user are suspected to be localized within occlusion areas and the size of the occlusion areas can be reliably estimated based upon depth and/or occlusion information (and possibly associated confidence metrics), the size of the neighborhood is initially determined by the estimated size of the occlusion area in pixels. In a number of embodiments, confidence information concerning the depth estimates can be utilized to refine the neighborhood by including all pixels with low confidence depth estimates within the neighborhood of the selected pixels.
The selected (512) region or neighborhood can be (optionally) validated by providing visual feedback to the user indicating the selected region of the light field image. In many embodiments, the user is permitted to grow and/or shrink (516) the selected region via additional user inputs prior to the final selection. In several embodiments, region growing/shrinking is enabled automatically and the selection is based upon an initial small window (such as a 3×3 neighborhood), and the region is grown interactively with adaptive support while the user maintains the selection.
Although specific processes for selecting regions containing artifacts are disclosed above with respect to
Updating Depth Estimates and Visibility Information
A variety of techniques can be utilized to determine the manner in which to update the depth estimates and/or visibility information for pixels that contribute to an artifact in a light field image in accordance with embodiments of the invention. In many embodiments, depth and/or visibility information can be provided directly by a user via an input mechanism enabling the identification of an object having the same depth and/or that is likely to exhibit the same visibility pattern in the captured image data. In other embodiments, the user can provide depth information directly using a user interface input mechanism including (but not limited to) a slider that allows selection of the correct depth from all possible depths. In several embodiments, image portions re-rendered using a variety of predetermined depth estimates and/or visibility patterns are presented to the user and the user selects the combination that results in the greatest reduction in the impact of the artifact. In many embodiments, the number of depths is extremely large and the process of selecting the correct depth can be performed using a hierarchy of selections from coarse to fine depth. In a number of embodiments, the depth is indicated through a measuring device or input directly using any available input method.
A process for automatically determining depths to apply to pixels contributing to an artifact within a selected region of a light field image based upon depth information provided via a user selection of a region of the image in accordance with an embodiment of the invention is illustrated in
In a number of embodiments, depth is determined by averaging the depths of pixels within the selected region. Prior to averaging, the depths of pixels within the selected region can be filtered based upon factors including the confidence of the depth estimates (612), and/or whether a given depth estimate is an outlier (614) relative to other pixels within the selected region. When the filtering processes (if any) are complete, the remaining pixels can be averaged (616) to provide a depth estimate that can be applied to the pixels within the region of the light field image containing the artifact. If no pixels remain within the selected region following filtering, then the selected region can be determined to be too unreliable to utilize as the basis of a depth estimate and the user can be prompted to select another region and/or an alternative process can be utilized to obtain a depth estimate for the region containing the identified artifact.
Once a new depth estimate has been obtained, processes such as (but not limited to) those disclosed in U.S. patent application Ser. No. 13/972,881 can be utilized to determine (618) the visibility of image data utilized to synthesize the region of the image containing the artifact identified by the user. A portion of the light field image containing the artifact can then be rendered using the updated depth estimates and visibility information in the manner outlined above. In many embodiments, a number of different renderings based upon image data captured by different subsets of cameras that each correspond to a different pattern of visibility within the scene can be generated and the visibility of image data within the light field can be determined based upon a user selection of one of the renderings.
In many embodiments, the subsets of cameras utilized to select the image data for each of the renderings is determined to correspond to specific patterns of visibility that occur within natural scenes. The clusters or groupings of cameras utilized to detect particular patterns of visibility within a scene can depend upon the numbers of cameras in an array camera, the camera that is selected as the reference camera, and/or the distribution of cameras from different color channels within the array. Eight groups of cameras in a 5×5 array corresponding to different patterns of visibility that are likely to be present within a scene with respect to pixels in a reference camera located at the center of the array are shown in
Although specific groups are shown in
In smaller array cameras, such as (but not limited to) 4×4 array cameras, and depending upon the pattern of color filters utilized within the array, it may not be possible to select groups of cameras that contain the same number of cameras in each color channel. In several embodiments, a color filter pattern is utilized so that groups of cameras corresponding to common visibility patterns contain the same number of cameras in a single color channel. In this way, image data captured within the color channel can be utilized to estimate depths for occluded or otherwise mismatched pixels by comparing the filtered costs of depth estimates obtained using the different subgroups. Four groups of cameras in a 4×4 array corresponding to different patterns of visibility that are likely to be present within a scene with respect to pixels in a reference camera located at the center of the array are shown in
Although specific processes for estimating depth and visibility are discussed with reference to
In several embodiments, an exhaustive or semi-exhaustive search of all possible depths is performed for the selected region containing the artifact. In some embodiments, the depth is automatically determined by the means of probing all possible depths in combination with all possible visibility patterns. A semi-exhaustive search is a variant in which some combinations of depths and visibility, which are less likely or not useful in reducing artifacts in the final rendered images are excluded from the search. In several embodiments, the results of a parallax search that gives the best overall match for the pixels in the region of the light field image containing the selected artifact is automatically selected as the updated depth estimate and/or visibility information for the pixels within the region of the image containing the artifact.
In many embodiments, the search is only performed exhaustively on depth, and the camera visibility pattern is computed automatically based upon the updated depth estimate in the manner described above with reference to
Reduction of Artifacts in a Light Field Image
An example of a light field image containing artifacts and a correction of one of the artifacts utilizing a process similar to those outlined above in accordance with an embodiment of the invention is illustrated in
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 can be practiced otherwise than specifically described 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. Accordingly, the scope of the invention should be determined not by the embodiments illustrated, but by the appended claims and their equivalents.
This application claims the benefit under 35 C. §119(e) of Provisional Patent Application No. 61/701,044, entitled “Method and Apparatus for Selection, Detection, and Correction of Depth Related Artifacts in Light Field Images” filed on Sep. 14, 2012, the disclosure of which is hereby incorporated by reference in its entirety.
| 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 |
| 5005083 | Grage et al. | Apr 1991 | A |
| 5070414 | Tsutsumi | Dec 1991 | A |
| 5144448 | Hornbaker | Sep 1992 | A |
| 5327125 | Iwase et al. | Jul 1994 | A |
| 5629524 | Stettner et al. | May 1997 | A |
| 5808350 | Jack et al. | Sep 1998 | A |
| 5832312 | Rieger et al. | Nov 1998 | A |
| 5880691 | Fossum et al. | Mar 1999 | A |
| 5933190 | Dierickx et al. | Aug 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 |
| 6097394 | Levoy et al. | Aug 2000 | A |
| 6124974 | Burger | Sep 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 | Jan 2001 | B1 |
| 6175379 | Uomori et al. | Jan 2001 | B1 |
| 6205241 | Melen | Mar 2001 | B1 |
| 6239909 | Hayashi et al. | May 2001 | B1 |
| 6358862 | Ireland et al. | Mar 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 |
| 6563537 | Kawamura et al. | May 2003 | B1 |
| 6571466 | Glenn et al. | Jun 2003 | B1 |
| 6603513 | Berezin | Aug 2003 | B1 |
| 6611289 | Yu et al. | Aug 2003 | B1 |
| 6627896 | Hashimoto et al. | Sep 2003 | B1 |
| 6628330 | Lin | Sep 2003 | B1 |
| 6635941 | Suda | Oct 2003 | B2 |
| 6639596 | Shum et al. | Oct 2003 | B1 |
| 6657218 | Noda | Dec 2003 | B2 |
| 6671399 | Berestov | Dec 2003 | 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 |
| 6795253 | Shinohara | Sep 2004 | B2 |
| 6819358 | Kagle et al. | Nov 2004 | B1 |
| 6879735 | Portniaguine et al. | Apr 2005 | B1 |
| 6903770 | Kobayashi et al. | Jun 2005 | B1 |
| 6909121 | Nishikawa | Jun 2005 | B2 |
| 6958862 | Joseph | Oct 2005 | B1 |
| 7015954 | Foote et al. | Mar 2006 | B1 |
| 7085409 | Sawhney et al. | Aug 2006 | B2 |
| 7161614 | Yamashita et al. | Jan 2007 | B1 |
| 7199348 | Olsen et al. | Apr 2007 | B2 |
| 7235785 | Hornback et al. | Jun 2007 | B2 |
| 7262799 | Suda | Aug 2007 | B2 |
| 7292735 | Blake et al. | Nov 2007 | B2 |
| 7295697 | Satoh | Nov 2007 | B1 |
| 7369165 | Bosco et al. | May 2008 | B2 |
| 7391572 | Jacobowitz et al. | Jun 2008 | B2 |
| 7408725 | Sato | Aug 2008 | B2 |
| 7606484 | Richards et al. | Oct 2009 | B1 |
| 7633511 | Shum et al. | Dec 2009 | B2 |
| 7639435 | Chiang | Dec 2009 | B2 |
| 7646549 | Zalevsky et al. | Jan 2010 | B2 |
| 7657090 | Omatsu et al. | Feb 2010 | B2 |
| 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 |
| 7782364 | Smith | Aug 2010 | B2 |
| 7826153 | Hong | Nov 2010 | B2 |
| 7840067 | Shen et al. | Nov 2010 | B2 |
| 7912673 | Hébert et al. | Mar 2011 | B2 |
| 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 |
| 8077245 | Adamo et al. | Dec 2011 | B2 |
| 8098297 | Crisan et al. | Jan 2012 | B2 |
| 8098304 | Pinto et al. | Jan 2012 | B2 |
| 8106949 | Tan et al. | Jan 2012 | B2 |
| 8126279 | Marcellin et al. | Feb 2012 | B2 |
| 8130120 | Kawabata et al. | Mar 2012 | B2 |
| 8131097 | Lelescu et al. | Mar 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 | May 2012 | B1 |
| 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 |
| 8289409 | Chang | Oct 2012 | B2 |
| 8290358 | Georgiev | Oct 2012 | B1 |
| 8294099 | Blackwell, Jr. | 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 et al. | Mar 2013 | B1 |
| 8406562 | Bassi et al. | Mar 2013 | B2 |
| 8446492 | Nakano et al. | May 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 |
| 8559756 | Georgiev et al. | Oct 2013 | B2 |
| 8581995 | Lin et al. | Nov 2013 | B2 |
| 8619082 | Ciurea et al. | Dec 2013 | B1 |
| 8648918 | Kauker et al. | Feb 2014 | B2 |
| 8655052 | Spooner et al. | Feb 2014 | B2 |
| 8682107 | Yoon et al. | Mar 2014 | B2 |
| 8692893 | McMahon | Apr 2014 | B2 |
| 8773536 | Zhang | Jul 2014 | B1 |
| 8780113 | Ciurea et al. | Jul 2014 | B1 |
| 8804255 | Duparre | Aug 2014 | B2 |
| 8830375 | Ludwig | Sep 2014 | B2 |
| 8831367 | Venkataraman et al. | 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 |
| 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 |
| 9019426 | Han et al. | Apr 2015 | B2 |
| 9025894 | Venkataraman et al. | May 2015 | B2 |
| 9025895 | Venkataraman et al. | May 2015 | B2 |
| 9031335 | Venkataraman et al. | May 2015 | B2 |
| 9031342 | Venkataraman et al. | May 2015 | B2 |
| 9031343 | Venkataraman et al. | May 2015 | B2 |
| 9036928 | Venkataraman et al. | 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 |
| 20010005225 | Clark et al. | Jun 2001 | A1 |
| 20010019621 | Hanna et al. | Sep 2001 | A1 |
| 20010038387 | Tomooka et al. | Nov 2001 | A1 |
| 20020012056 | Trevino | Jan 2002 | A1 |
| 20020027608 | Johnson | Mar 2002 | A1 |
| 20020039438 | Mori et al. | Apr 2002 | A1 |
| 20020063807 | Margulis | May 2002 | A1 |
| 20020087403 | Meyers et al. | Jul 2002 | A1 |
| 20020089596 | Suda | Jul 2002 | A1 |
| 20020094027 | Sato et al. | Jul 2002 | A1 |
| 20020101528 | Lee et al. | Aug 2002 | A1 |
| 20020113867 | Takigawa et al. | Aug 2002 | A1 |
| 20020113888 | Sonoda et al. | Aug 2002 | A1 |
| 20020163054 | Suda et al. | Nov 2002 | A1 |
| 20020167537 | Trajkovic | Nov 2002 | A1 |
| 20020177054 | Saitoh et al. | Nov 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 |
| 20030179418 | Wengender et al. | Sep 2003 | A1 |
| 20030190072 | Adkins et al. | Oct 2003 | A1 |
| 20030211405 | Venkataraman | Nov 2003 | A1 |
| 20040008271 | Hagimori et al. | Jan 2004 | A1 |
| 20040012689 | Tinnerino et al. | 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 |
| 20040100570 | Shizukuishi | May 2004 | A1 |
| 20040114807 | Lelescu et al. | Jun 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 |
| 20040179834 | Szajewski et al. | Sep 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 |
| 20040240052 | Minefuji et al. | Dec 2004 | A1 |
| 20040251509 | Choi | Dec 2004 | A1 |
| 20040264806 | Herley | Dec 2004 | A1 |
| 20050006477 | Patel | Jan 2005 | A1 |
| 20050009313 | Suzuki 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 |
| 20050128595 | Shimizu | Jun 2005 | A1 |
| 20050132098 | Sonoda et al. | 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 |
| 20050175257 | Kuroki | Aug 2005 | A1 |
| 20050185711 | Pfister et al. | Aug 2005 | A1 |
| 20050205785 | Hornback et al. | Sep 2005 | A1 |
| 20050219363 | Kohler et al. | Oct 2005 | A1 |
| 20050225654 | Feldman et al. | Oct 2005 | A1 |
| 20050275946 | Choo et al. | Dec 2005 | A1 |
| 20050286612 | Takanashi | Dec 2005 | A1 |
| 20060002635 | Nestares et al. | Jan 2006 | A1 |
| 20060023197 | Joel | Feb 2006 | A1 |
| 20060023314 | Boettiger et al. | Feb 2006 | A1 |
| 20060033005 | Jerdev et al. | Feb 2006 | A1 |
| 20060034003 | Zalevsky | Feb 2006 | A1 |
| 20060038891 | Okutomi et al. | Feb 2006 | A1 |
| 20060049930 | Zruya 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 |
| 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 |
| 20060203113 | Wada et al. | Sep 2006 | A1 |
| 20060210186 | Berkner | Sep 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 |
| 20070002159 | Olsen et al. | Jan 2007 | A1 |
| 20070024614 | Tam | 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 |
| 20070102622 | Olsen et al. | May 2007 | A1 |
| 20070126898 | Feldman | Jun 2007 | A1 |
| 20070127831 | Venkataraman | Jun 2007 | A1 |
| 20070139333 | Sato et al. | Jun 2007 | A1 |
| 20070146511 | Kinoshita et al. | Jun 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 |
| 20070206241 | Smith et al. | Sep 2007 | A1 |
| 20070211164 | Olsen et al. | Sep 2007 | A1 |
| 20070216765 | Wong et al. | Sep 2007 | A1 |
| 20070228256 | Mentzer 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 |
| 20070263114 | Gurevich et al. | Nov 2007 | A1 |
| 20070268374 | Robinson | Nov 2007 | A1 |
| 20070296832 | Ota et al. | Dec 2007 | A1 |
| 20070296835 | Olsen et al. | Dec 2007 | A1 |
| 20070296847 | Chang et al. | Dec 2007 | A1 |
| 20080019611 | Larkin | Jan 2008 | A1 |
| 20080025649 | Liu et al. | Jan 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 |
| 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 |
| 20080112635 | Kondo 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 |
| 20080158259 | Kempf et al. | Jul 2008 | A1 |
| 20080158375 | Kakkori et al. | Jul 2008 | A1 |
| 20080158698 | Chang et al. | Jul 2008 | A1 |
| 20080187305 | Raskar et al. | Aug 2008 | A1 |
| 20080193026 | Horie et al. | Aug 2008 | A1 |
| 20080218610 | Chapman 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 |
| 20080298674 | Baker et al. | Dec 2008 | A1 |
| 20090050946 | Duparre et al. | Feb 2009 | A1 |
| 20090052743 | Techmer | Feb 2009 | A1 |
| 20090060281 | Tanida et al. | Mar 2009 | A1 |
| 20090086074 | Li et al. | Apr 2009 | A1 |
| 20090091806 | Inuiya | Apr 2009 | A1 |
| 20090096050 | Park | Apr 2009 | A1 |
| 20090102956 | Georgiev | Apr 2009 | A1 |
| 20090109306 | Shan et al. | Apr 2009 | A1 |
| 20090128833 | Yahav | May 2009 | A1 |
| 20090140131 | Utagawa et al. | Jun 2009 | A1 |
| 20090152664 | Klem et al. | Jun 2009 | A1 |
| 20090167922 | Perlman 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 |
| 20090225203 | Tanida et al. | Sep 2009 | A1 |
| 20090237520 | Kaneko et al. | Sep 2009 | A1 |
| 20090263017 | Tanbakuchi | Oct 2009 | A1 |
| 20090268192 | Koenck et al. | Oct 2009 | A1 |
| 20090268970 | Babacan et al. | Oct 2009 | A1 |
| 20090268983 | Stone | Oct 2009 | A1 |
| 20090274387 | Jin | Nov 2009 | A1 |
| 20090284651 | Srinivasan | Nov 2009 | A1 |
| 20090297056 | Lelescu et al. | Dec 2009 | A1 |
| 20090302205 | Olsen et al. | Dec 2009 | A9 |
| 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 |
| 20100013927 | Nixon | Jan 2010 | A1 |
| 20100053342 | Hwang 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 |
| 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 |
| 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 |
| 20100133230 | Henriksen et al. | Jun 2010 | A1 |
| 20100133418 | Sargent et al. | Jun 2010 | A1 |
| 20100141802 | Knight et al. | Jun 2010 | A1 |
| 20100142839 | Lakus-Becker | Jun 2010 | A1 |
| 20100157073 | Kondo et al. | Jun 2010 | A1 |
| 20100165152 | Lim | Jul 2010 | A1 |
| 20100166410 | Chang et al. | Jul 2010 | A1 |
| 20100177411 | Hegde et al. | Jul 2010 | A1 |
| 20100194901 | Van Hoorebeke et al. | Aug 2010 | A1 |
| 20100195716 | Klein et al. | Aug 2010 | A1 |
| 20100201834 | Maruyama et al. | Aug 2010 | A1 |
| 20100208100 | Olsen et al. | Aug 2010 | A9 |
| 20100220212 | Perlman et al. | Sep 2010 | A1 |
| 20100231285 | Boomer et al. | Sep 2010 | A1 |
| 20100244165 | Lake et al. | Sep 2010 | A1 |
| 20100265381 | Yamamoto et al. | Oct 2010 | A1 |
| 20100265385 | Knight et al. | Oct 2010 | A1 |
| 20100281070 | Chan et al. | Nov 2010 | A1 |
| 20100302423 | Adams, Jr. et al. | Dec 2010 | A1 |
| 20100309292 | Ho et al. | Dec 2010 | A1 |
| 20100321595 | Chiu | Dec 2010 | A1 |
| 20110001037 | Tewinkle | Jan 2011 | A1 |
| 20110018973 | Takayama | Jan 2011 | A1 |
| 20110032370 | Ludwig | Feb 2011 | A1 |
| 20110043661 | Podoleanu | Feb 2011 | A1 |
| 20110043665 | Ogasahara | Feb 2011 | A1 |
| 20110043668 | McKinnon et al. | Feb 2011 | A1 |
| 20110069189 | Venkataraman et al. | Mar 2011 | A1 |
| 20110080487 | Venkataraman et al. | Apr 2011 | A1 |
| 20110108708 | Olsen et al. | May 2011 | A1 |
| 20110121421 | Charbon et al. | May 2011 | A1 |
| 20110122308 | Duparre | May 2011 | A1 |
| 20110128393 | Tavi et al. | Jun 2011 | A1 |
| 20110128412 | Milnes et al. | Jun 2011 | A1 |
| 20110149408 | Hahgholt et al. | Jun 2011 | A1 |
| 20110149409 | Haugholt et al. | Jun 2011 | A1 |
| 20110153248 | Gu et al. | Jun 2011 | A1 |
| 20110157321 | Nakajima et al. | Jun 2011 | A1 |
| 20110176020 | Chang | Jul 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 |
| 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 | Oct 2011 | A1 |
| 20110255592 | Sung et al. | Oct 2011 | A1 |
| 20110255745 | Hodder et al. | Oct 2011 | A1 |
| 20110267348 | Lin et al. | Nov 2011 | A1 |
| 20110273531 | Ito et al. | Nov 2011 | A1 |
| 20110274366 | Tardif | Nov 2011 | A1 |
| 20110279721 | McMahon | Nov 2011 | A1 |
| 20110285866 | Bhrugumalla et al. | Nov 2011 | A1 |
| 20110298917 | Yanagita | Dec 2011 | A1 |
| 20110300929 | Tardif et al. | Dec 2011 | A1 |
| 20110310980 | Mathew | Dec 2011 | A1 |
| 20110317766 | Lim, II et al. | Dec 2011 | A1 |
| 20120012748 | Pain et al. | Jan 2012 | A1 |
| 20120026297 | Sato | Feb 2012 | A1 |
| 20120026342 | Yu et al. | Feb 2012 | A1 |
| 20120039525 | Tian et al. | Feb 2012 | A1 |
| 20120044249 | Mashitani et al. | Feb 2012 | A1 |
| 20120044372 | Cô tè et al. | Feb 2012 | A1 |
| 20120069235 | Imai | Mar 2012 | A1 |
| 20120105691 | Waqas et al. | May 2012 | A1 |
| 20120113413 | Miahczylowicz-Wolski 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 |
| 20120170134 | Bolis et al. | Jul 2012 | A1 |
| 20120176479 | Mayhew 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 |
| 20120200734 | Tang | Aug 2012 | A1 |
| 20120229628 | Ishiyama et al. | Sep 2012 | A1 |
| 20120249550 | Akeley et al. | Oct 2012 | A1 |
| 20120249836 | Ali et al. | Oct 2012 | A1 |
| 20120262607 | Shimura et al. | Oct 2012 | A1 |
| 20120287291 | McMahon | Nov 2012 | A1 |
| 20120293695 | Tanaka | Nov 2012 | A1 |
| 20120314033 | Lee 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 |
| 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 |
| 20130050504 | Safaee-Rad et al. | Feb 2013 | A1 |
| 20130050526 | Keelan | Feb 2013 | A1 |
| 20130057710 | Mcmahon | Mar 2013 | A1 |
| 20130070060 | Chatterjee et al. | Mar 2013 | A1 |
| 20130076967 | Brunner et al. | Mar 2013 | A1 |
| 20130077880 | Venkataraman et al. | Mar 2013 | A1 |
| 20130077882 | Venkataraman et al. | Mar 2013 | A1 |
| 20130088489 | Schmeitz et al. | Apr 2013 | A1 |
| 20130088637 | Duparre | Apr 2013 | A1 |
| 20130113899 | Morohoshi et al. | May 2013 | A1 |
| 20130120605 | Georgiev et al. | 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 |
| 20130147979 | McMahon et al. | Jun 2013 | A1 |
| 20130215108 | McMahon et al. | Aug 2013 | A1 |
| 20130222556 | Shimada | Aug 2013 | A1 |
| 20130223759 | Nishiyama et al. | Aug 2013 | A1 |
| 20130229540 | Farina et al. | Sep 2013 | A1 |
| 20130259317 | Gaddy | Oct 2013 | A1 |
| 20130265459 | Duparre et al. | Oct 2013 | A1 |
| 20130274923 | By et al. | Oct 2013 | A1 |
| 20130293760 | Nisenzon et al. | Nov 2013 | A1 |
| 20140009586 | McNamer et al. | Jan 2014 | A1 |
| 20140076336 | Clayton et al. | Mar 2014 | A1 |
| 20140079336 | Venkataraman et al. | Mar 2014 | A1 |
| 20140092281 | Nisenzon et al. | Apr 2014 | A1 |
| 20140104490 | Hsieh et al. | Apr 2014 | A1 |
| 20140118493 | Sali et al. | May 2014 | A1 |
| 20140132810 | McMahon | May 2014 | A1 |
| 20140176592 | Wilburn et al. | Jun 2014 | A1 |
| 20140192253 | Laroia | Jul 2014 | A1 |
| 20140198188 | Izawa | 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 |
| 20140267890 | Lelescu et al. | Sep 2014 | A1 |
| 20140285675 | Mullis | Sep 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 |
| 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 |
| 20150091900 | Yang et al. | Apr 2015 | A1 |
| 20150122411 | Rodda et al. | May 2015 | A1 |
| 20150124113 | Rodda et al. | May 2015 | A1 |
| 20150124151 | Rodda et al. | May 2015 | A1 |
| Number | Date | Country |
|---|---|---|
| 1839394 | Sep 2006 | CN |
| 840502 | May 1998 | EP |
| 1201407 | May 2002 | EP |
| 1734766 | Dec 2006 | EP |
| 2104334 | Sep 2009 | EP |
| 2336816 | Jun 2011 | EP |
| 59-025483 | Sep 1984 | JP |
| 64-037177 | Jul 1989 | JP |
| 02-285772 | Nov 1990 | JP |
| 11142609 | May 1999 | JP |
| 11223708 | Aug 1999 | JP |
| 2000209503 | Jul 2000 | JP |
| 2002205310 | Jul 2002 | JP |
| 2002252338 | Sep 2002 | JP |
| 2003094445 | Apr 2003 | JP |
| 2003163938 | Jun 2003 | JP |
| 2004221585 | Aug 2004 | JP |
| 2005116022 | Apr 2005 | JP |
| 2005181460 | Jul 2005 | JP |
| 2005295381 | Oct 2005 | JP |
| 2006033493 | Feb 2006 | JP |
| 2006047944 | Feb 2006 | JP |
| 2006258930 | Sep 2006 | JP |
| 2007520107 | Jul 2007 | JP |
| 2008055908 | Mar 2008 | JP |
| 2008507874 | Mar 2008 | JP |
| 2008258885 | Oct 2008 | JP |
| 2009132010 | Jun 2009 | JP |
| 2011109484 | Jun 2011 | JP |
| 2013526801 | Jun 2013 | JP |
| 2014521117 | Aug 2014 | JP |
| 1020110097647 | Aug 2011 | KR |
| 2007083579 | Jul 2007 | WO |
| 2008108271 | Sep 2008 | WO |
| 2008108926 | Sep 2008 | WO |
| 2008150817 | Dec 2008 | WO |
| 2009151903 | Dec 2009 | WO |
| 2011008443 | Jan 2011 | WO |
| 2011055655 | May 2011 | WO |
| 2011063347 | May 2011 | WO |
| 2011116203 | Sep 2011 | WO |
| 2011063347 | Oct 2011 | WO |
| 2011143501 | Nov 2011 | WO |
| 2012057619 | May 2012 | WO |
| 2012057620 | May 2012 | WO |
| 2012057621 | May 2012 | WO |
| 2012057622 | May 2012 | WO |
| 2012057623 | May 2012 | WO |
| 2012057620 | Jun 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 |
| 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 |
| 2014150856 | 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 |
| Entry |
|---|
| International Search Report and Written Opinion for International Application No. PCT/US13/59991, International Filing Date Sep. 16, 2013, Search Completed Feb. 6, 2014, Mailed Feb. 26, 2014, 8 pgs. |
| 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. |
| Shum et al., “Pop-Up Light Field: An Interactive Image-Based Modeling and Rendering System,” Apr. 2004, ACM Transactions on Graphics, 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. |
| LensVector, “How LensVector Autofocus Works”, http://www.lensvector.com/overview.html. |
| Levoy, “Light Fields and Computational Imaging”, IEEE Computer Society, Aug. 2006, pp. 46-55. |
| Levoy et al., “Light Field Rendering”, Proc. ADM SIGGRAPH '96, pp. 1-12. |
| Muehlebach, “Camera Auto Exposure Control for VSLAM Applications”, Studies on Mechatronics. |
| Nayar, “Computational Cameras: Redefining the Image”, IEEE Computer Society, Aug. 2006, pp. 30-38. |
| Ng, “Digital Light Field Photography”, Thesis, Jul. 2006, 203 pgs. |
| Ng et al., “Super-Resolution Image Restoration from Blurred Low-Resolution Images”, Journal of Mathematical Imaging and Vision, 2005, vol. 23, pp. 367-378. |
| 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, 2007, 12 pgs. |
| Park et al., “Super-Resolution Image Reconstruction”, IEEE Signal Processing Magazine, May 2003, pp. 21-36. |
| Pham et al., “Robust Super-Resolution without Regularization”, Journal of Physics: Conference Series 124, 2008, pp. 1-19. |
| Polight, “Designing Imaging Products Using Reflowable Autofocus Lenses”, http://www.polight.no/tunable-polymer-autofocus-lens-html--11.html. |
| Protter et al., “Generalizing the Nonlocal-Means to Super-Resolution Reconstruction”, IEEE Transactions on Image Processing, Jan. 2009, 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. |
| 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. |
| 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, 2007, pp. 208-215. |
| Sauer et al., “Parallel Computation of Sequential Pixel Updates in Statistical Tomographic Reconstruction”, ICIP 1995, pp. 93-96. |
| 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”, Source and date unknown, 8 pgs. |
| Takeda et al., “Super-resolution Without Explicit Subpixel Motion Estimation”, IEEE Transaction on Image Processing, Sep. 2009, vol. 18, No. 9, pp. 1958-1975. |
| 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. |
| Taylor, “Virtual camera movement: The way of the future?”, American Cinematographer 77, 9 (September), 93-100. |
| Vaish et al., “Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures”, Proceeding, CVPR'06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition—vol. 2, 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. |
| Vuong et al., “A New Auto Exposure and Auto White-Balance Algorithm to Detect High Dynamic Range Conditions Using CMOS Technology”. |
| Wang, “Calculation of Image Position, Size and Orientation Using First Order Properties”, 10 pgs. |
| 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, 2005, 5674, 12 pgs. |
| Wikipedia, “Polarizing Filter (Photography)”. |
| 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. 765-776. |
| Wilburn et al., “High-Speed Videography Using a Dense Camera Array”, Proceeding, CVPR'04 Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 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. |
| Yang et al., “A Real-Time Distributed Light Field Camera”, Eurographics Workshop on Rendering (2002), pp. 1-10. |
| Yang et al., “Superresolution Using Preconditioned Conjugate Gradient Method”, Source and date unknown, 8 pgs. |
| Zhang et al., “A Self-Reconfigurable Camera Array”, Eurographics Symposium on Rendering, 2004, 12 pgs. |
| Zomet et al., “Robust Super-Resolution”, IEEE, 2001, pp. 1-6. |
| Liu et al., “Virtual View Reconstruction Using Temporal Information”, 2012 IEEE International Conference on Multimedia and Expo, 2012, pp. 115-120. |
| 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, date unknown, 21 pgs. |
| Boye et al., “Comparison of Subpixel Image Registration Algorithms”, Proc. of SPIE—IS&T Electronic Imaging, vol. 7246, pp. 72460X-1-72460X-9. |
| Bruckner et al., “Artificial compound eye applying hyperacuity”, Optics Express, December 11, 2006, vol. 14, No. 25, pp. 12076-12084. |
| Bruckner et al., “Driving microoptical imaging systems towards miniature camera applications”, Proc. SPIE, Micro-Optics, 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. |
| Capel, “Image Mosaicing and Super-resolution”, [online], 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>, Title pg., abstract, table of contents, pp. 1-263 (269 total pages), 2001. |
| Chan et al., “Extending the Depth of Field in a Compound-Eye Imaging System with Super-Resolution Reconstruction”, Proceedings—International Conference on Pattern Recognition, 2006, vol. 3, pp. 623-626. |
| Chan et al., “Investigation of Computational Compound-Eye Imaging System with Super-Resolution Reconstruction”, IEEE, ISASSP 2006, pp. 1177-1180. |
| Chan et al., “Super-resolution reconstruction in a computational compound-eye imaging system”, Multidim. Syst. Sign Process, 2007, vol. 18, pp. 83-101. |
| Drouin et al., “Fast Multiple-Baseline Stereo with Occlusion”, Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling, 2005, 8 pgs. |
| Drouin et al., “Geo-Consistency for Wide Multi-Camera Stereo”, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 8 pgs. |
| Drouin et al., “Improving Border Localization of Multi-Baseline Stereo Using Border-Cut”, International Journal of Computer Vision, Jul. 2009, vol. 83, Issue 3, 8 pgs. |
| 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, 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. 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, 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, 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, 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, 3005, vol. 44, No. 15, pp. 2949-2956. |
| Duparre et al., “Ultra-Thin Camera Based on Artificial Apposistion Compound Eyes”, 10th Microoptics Conference, Sep. 1-3, 2004, 2 pgs. |
| Fanaswala, “Regularized Super-Resolution of Multi-View Images”, Retrieved on Nov. 10, 2012. Retrieved from the Internet at URL:<http://www.site.uottawa.ca/-edubois/theses/Fanaswala—thesis.pdf>, 163 pgs., Aug. 2009. |
| Farrell et al., “Resolution and Light Sensitivity Tradeoff with Pixel Size”, Proceedings of the SPIE Electronic Imaging 2006 Conference, 2006, vol. 6069, 8 pgs. |
| Farsiu et al., “Advances and Challenges in Super-Resolution”, International Journal of Imaging Systems and Technology, 2004, vol. 14, pp. 47-57. |
| Farsiu et al., Fast and Robust Multiframe Super Resolution, IEEE Transactions on Image Processing, Oct. 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, pp. 141-159. |
| 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, pp. 191-198. |
| Fischer et al., Optical System Design, 2nd Edition, SPIE Press, pp. 49-58. |
| 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, vol. 16, No. 12, pp. 2953-2964. |
| Horisaki et al., “Irregular Lens Arrangement Design to Improve Imaging Performance of Compound-Eye Imaging Systems”, Applied Physics Express, 2010, vol. 3, pp. 022501-1-022501-3. |
| Horisaki et al., “Superposition Imaging for Three-Dimensionally Space-Invariant Point Spread Functions”, Applied Physics Express, 2011, vol. 4, pp. 112501-1-112501-3. |
| Kang et al., “Handling Occlusions inn Dense Multi-View Stereo”, Computer Vision and Pattern Recognition, 2001, vol. 1, pp. I-103-I-110. |
| 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. |
| Krishnamurthy et al., “Compression and Transmission of Depth Maps for Image-Based Rendering”, Image Processing, 2001, pp. 828-831. |
| Kutulakos et al., “Occluding Contour Detection Using Affine Invariants and Purposive Viewpoint Control”, Proc., CVPR 94, 8 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US13/48772, Search Completed Oct. 21, 2013, Mailed Nov. 8, 2013, 6 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US13/56065, Search Completed Nov. 25, 2013, Mailed Nov. 26, 2013, 8 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US2013/024987, Search Completed Mar. 27, 2013, Mailed Apr. 15, 2013, 14 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US2013/056502, Search Completed Feb. 18, 2014, Mailed Mar. 19, 2014, 7 pgs. |
| International Search Report and Written Opinion for International Application PCT/US13/069932, Search Completed Mar. 14, 2014, Mailed Apr. 14, 2014, 12 pgs. |
| IPRP for International Application No. PCT/US2012/059813, International Filing Date Oct. 11, 2012, Search Completed Apr. 15, 2014, 7 pgs. |
| Search Report and Written Opinion for International Application PCT/US11/36349, mailed Aug. 22, 2011, 12 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US2011/64921, Report Completed Feb. 25, 2011, mailed Mar. 6, 2012, 17 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US2013/027146, completed Apr. 2, 2013, 12 pgs. |
| International Search Report and Written Opinion for International Application PCT/US2009/044687, completed Jan. 5, 2010, 13 pgs. |
| International Search Report and Written Opinion for International Application PCT/US2010/057661, completed Mar. 9, 2011, 14 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/US2012/059813, completed Dec. 17, 2012, 8 pgs. |
| International Search Report and Written Opinion for International Application PCT/US12/37670, Mailed Jul. 18, 2012, Search Completed Jul. 5, 2012, 9 pgs. |
| International Search Report and Written Opinion for International Application PCT/US2012/58093, completed Nov. 15, 2012, 12 pgs. |
| Office Action for U.S. Appl. No. 12/952,106, dated Aug. 16, 2012, 12 pgs. |
| 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. |
| Bertero et al., “Super-resolution in computational imaging”, Micron, 2003, vol. 34, Issues 6-7, 17 pgs. |
| Bishop et al., “Full-Resolution Depth Map Estimation from an Aliased Plenoptic Light Field”, ACCV 2010, Part II, LNCS 6493, pp. 186-200, 2011. |
| Bishop et al., “Light Field Superresolution”, Retrieved from http://home.eps.hw.ac.uk/˜sz73/ICCP09/LightFieldSuperresolution.pdf, 9 pp. 9. |
| 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, pp. 972-986. |
| Borman, “Topics in Multiframe Superresolution Restoration”, Thesis of Sean Borman, Apr. 2004, 282 pgs. |
| Borman et al, “Image Sequence Processing”, Source unknown, 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. 1998, 3653, 10 pgs. |
| Borman et al., “Image Resampling and Constraint Formulation for Multi-Frame Super-Resolution Restoration”, Proc. SPIE, Jun. 2003, 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 2004, vol. 5299, 12 pgs. |
| Borman et al., “Nonlinear Prediction Methods for Estimation of Clique Weighting Parameters in NonGaussian Image Models”, Proc. SPIE, 1998, 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. |
| US 8,957,977, 2/2015, Venkataraman et al. (withdrawn). |
| US 8,964,053, 2/2015, Venkataraman et al. (withdrawn). |
| US 8,965,058, 2/2015, Venkataraman et al. (withdrawn). |
| US 9,014,491, 4/2015, Venkataraman et al. (withdrawn). |
| International Search Report and Written Opinion for International Application PCT/US2014/064693, Completed Mar. 7, 2015, Mailed Apr. 2, 2015, 15 pgs. |
| Extended European Search Report for European Application EP12835041.0, Report Completed Jan. 28, 2015, Mailed Feb. 4, 2015, 6 Pgs. |
| International Preliminary Report on Patentability for International Application No. PCT/US2013/059991, Issued Mar. 17, 2015, Mailed Mar. 26, 2015, 8 pgs. |
| International Preliminary Report on Patentability for International Application PCT/US13/62720, Report Issued Mar. 31, 2015, Mailed Apr. 9, 2015, 8 Pgs. |
| International Preliminary Report on Patentability for International Application PCT/US2013/039155, completed Nov. 4, 2014, Mailed Nov. 13, 2014, 10 Pgs. |
| International Preliminary Report on Patentability for International Application PCT/US2013/048772, issued Dec. 31, 2014, Mailed Jan. 8, 2015, 8 Pgs. |
| International Preliminary Report on Patentability for International Application PCT/US2014/023762, issued Mar. 2, 2015, Mailed Mar. 9, 2015, 10 Pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US13/46002, completed Nov. 13, 2013, Mailed Nov. 29, 2013, 7 pgs. |
| International Search Report and Written Opinion for International Application No. PCT/US2013/039155, completed Jul. 1, 2013, Mailed Jul. 11, 2013, 11 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US13/62720, completed Mar. 25, 2014, Mailed Apr. 21, 2014, 9 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US14/17766, report completed May 28, 2014, Mailed Jun. 18, 2014, 9 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US14/18084, completed May 23, 2014, Mailed Jun. 10, 2014, 12 pgs. |
| International Search Report and Written Opinion for International Application PCT/US14/18116, completed May 13, 2014, Mailed Jun. 2, 2014, 12 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US14/22118, report completed Jun. 9, 2014, Mailed, Jun. 25, 2014, 5 pgs. |
| International Search Report and Written Opinion for International Application PCT/US14/24407, report completed Jun. 11, 2014, Mailed Jul. 8, 2014, 9 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US14/25100, report completed Jul. 7, 2014, Mailed Aug. 7, 2014 5 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US2014/022123, completed Jun. 9, 2014, Mailed Jun. 25, 2014, 5 pgs. |
| International Search Report and Written Opinion for International Application PCT/US2014/030692, completed Jul. 28, 2014, Mailed Aug. 27, 2014, 7 Pages. |
| International Search Report and Written Opinion for International Application PCT/US2014/066229, Completed Mar. 6, 2015, Mailed Mar. 19, 2015, 9 Pgs. |
| International Search Report and Written Opinion for International Application PCT/US2014/23762, Completed May 30, 2014, Mailed Jul. 3, 2014, 6 Pgs. |
| International Preliminary Report on Patentability for International Application PCT/US13/56065, Issued Feb. 24, 2015, Mailed Mar. 5, 2015, 4 Pgs. |
| International Preliminary Report on Patentability for International Application PCT/US2013/056502, Issued Feb. 24, 2015, Mailed Mar. 5, 2015, 7 Pgs. |
| Chen et al., “Interactive deformation of light fields”, In Proceedings of SIGGRAPH I3D 2005, pp. 139-146. |
| Goldman et al., “Video Object Annotation, Navigation, and Composition”, In Proceedings of UIST 2008, pp. 3-12. |
| Gortler et al., “The Lumigraph”, In Proceedings of SIGGRAPH 1996, pp. 43-54. |
| Hacohen et al., “Non-Rigid Dense Correspondence with Applications for Image Enhancement”, ACM Transactions on Graphics, 30, 4, 2011, pp. 70:1-70:10. |
| Hasinoff et al., “Search-and-Replace Editing for Personal Photo Collections”, Computational Photography (ICCP) 2010, pp. 1-8. |
| Horn et al., “LightShop: Interactive Light Field Manipulation and Rendering”, In Proceedings of I3D 2007, pp. 121-128. |
| Isaksen et al., “Dynamically Reparameterized Light Fields”, In Proceedings of SIGGRAPH 2000, pp. 297-306. |
| Jarabo et al., “Efficient Propagation of Light Field Edits”, In Proceedings of SIACG 2011, pp. 75-80. |
| Josh et al., “Synthetic Aperture Tracking: Tracking Through Occlusions”, I CCV IEEE 11th International Conference on Computer Vision; Publication [online]. Oct. 2007 [retrieved Jul. 28, 2014]. Retrieved from the Internet: <URL: http:I/ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4409032&isnumber=4408819>; pp. 1-8. |
| Lo et al., “Stereoscopic 3D Copy & Paste”, ACM Transactions on Graphics, vol. 29, No. 6, Article 147, Dec. 2010, pp. 147:1-147:10. |
| Seitz et al., “Plenoptic Image Editing”, International Journal of Computer Vision 48, 2, pp. 115-129. |
| 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. |
| 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. |
| Number | Date | Country | |
|---|---|---|---|
| 20140079336 A1 | Mar 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61701044 | Sep 2012 | US |
