Systems and methods for image data compression

Information

  • Patent Grant
  • 9521416
  • Patent Number
    9,521,416
  • Date Filed
    Tuesday, March 11, 2014
    10 years ago
  • Date Issued
    Tuesday, December 13, 2016
    7 years ago
Abstract
Systems and methods for image data compression in an array camera are disclosed. An array camera includes a processor, an array of cameras, and a compression module. The compression module is configured to receive image data from the array of pixels of a focal plane in a sequentially linear group of pixel data, analyze the received image data based upon truncation rules, compress the received data image based upon the analysis to generate compressed image data, and generate a bit mask identifying truncated image data in the compressed image data.
Description
FIELD OF THE INVENTION

The present invention is generally related to array cameras and more specifically to data compression of image data captured by focal planes within an array camera for bandwidth reduction.


BACKGROUND OF THE INVENTION

In a typical camera, light enters through an opening (aperture) at one end of the camera and is directed to a focal plane by a lens stack. The lens stack creates an optical channel that forms an image of a scene upon the focal plane. The focal plane includes an array of light sensitive pixels, which are part of a sensor that generates signals upon receiving light via the optical channel. Commonly used sensors include CCD (charge-coupled device) sensors and CMOS (complementary metal-oxide-semiconductor) sensors.


Traditional cameras typically use a single focal plane to capture single images, one at a time. The image data from each pixel of the focal plane is then sent directly from the focal plane to a processor. The processor can manipulate the image data, such as to encode or modify the image data.


SUMMARY OF THE INVENTION

Systems and methods for image data compression in an array camera are disclosed. In accordance with embodiments of the invention, an array camera includes a processor, a camera module, and a compression module. The camera module includes an imager array and an optic array of lens stacks. Each focal plane of the imager array includes rows of pixels that also form columns of pixels and is contained within a region of the imager array that does not contain pixels from another focal plane. An image is formed on each active focal plane by a separate lens stack in said optic array of lens stacks. A compression module is configured to receive image data from the array of pixels of a focal plane in a sequentially linear group of pixel data, analyze the received image data based upon truncation rules, compress the received data image based upon the analysis to generate compressed image data, and generate a bit mask identifying truncated image data in the compressed image data.





BRIEF DESCRIPTION OF THE DRAWINGS


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



FIG. 1B conceptually illustrates a focal plane from an imager array in accordance with an embodiment of the invention.



FIG. 2 conceptually illustrates an optic array and an imager array in an array camera module in accordance with an embodiment of the invention.



FIG. 3 is an architecture diagram of an imager array in accordance with an embodiment of the invention.



FIG. 4 illustrates a process for transmitting compressed image data in accordance with an embodiment of the invention.



FIG. 5 illustrates a process for decompressing image data compressed in accordance with an embodiment of the invention.



FIG. 6 illustrates image data transmitted as packets in accordance with an embodiment of the invention.



FIG. 7 illustrates transmission of image data as packets compatible with the MIPI CSI-2 standard interface format in accordance with an embodiment of the invention.



FIGS. 8a, 8b, and 8c conceptually illustrate lines of image data transmitted by interleaving image data from multiple focal planes using modulo processes in accordance with an embodiment of the invention.





DETAILED DESCRIPTION

Turning now to the drawings, systems and methods for compressing image data captured by individual pixels from focal planes of an imager array in an array camera in accordance with embodiments of the invention are illustrated. Array cameras are discussed in U.S. patent application Ser. No. 13/106,797 entitled “Architectures for Imager Arrays and Array Cameras” and U.S. patent application Ser. No. 12/952,106 entitled “Capturing and Processing of Images Using Monolithic Camera Array with Heterogeneous Imagers” the disclosures of both applications are hereby incorporated by reference in their entirety. In several embodiments, an array camera includes a camera module constructed using an imager array and a lens stack array, which combine to form a plurality of cameras. The cameras capture image data, which can be provided via an interface to a processor that can perform processes including synthesizing high resolution images from the individual images captured by the cameras in the camera module using super-resolution processing. Super-resolution processing is discussed in U.S. patent application Ser. No. 12/967,807 entitled “Systems and Methods for Synthesizing High Resolution Images Using Super-Resolution Processes”, filed Dec. 14, 2010, the disclosure of which is hereby incorporated by reference in its entirety.


In several embodiments, compressed image data is truncated image data along with at least one associated bitmask. In certain embodiments, image data is truncated by removing redundant or unwanted image data generated by one or more focal planes while a bitmask is generated that notes the truncation for reference in conjunction with the truncated image data. A processor using a decompression process can utilize a bit mask and compressed image data to reconstruct image data captured by the focal planes of an array camera. In many embodiments, the image data is truncated using a compression module to identify redundant image data. While much of the discussion that follows relates to truncation of image data captured by a single focal plane, similar processes can be utilized to truncate image data captured by multiple focal planes (e.g. image data from different focal planes that sample similar portions of the object space of a scene). Accordingly, truncation processes in accordance with embodiments of the invention should be understood as not limited to application image data captured by a single focal plan.


In a number of embodiments, digital logic is utilized to analyze image data in a compression module to determine whether the image data can be compressed by truncating redundant image data. The digital logic can be configured in accordance with any criteria for data compression, including (but not limited to) whether a pixel value is redundant with respect to neighboring pixel values or simply to omit a certain amount of image data to comply with certain bandwidth constraints. In several embodiments, redundancy can be determined based upon adjacent pixel values having the same intensity value, intensity values within a predetermined threshold distance, and/or intensity values within a threshold based upon the noise characteristics of the sensor. Each pixel value can be analyzed to determine if the image data should be transmitted or omitted. The decision can be stored in a digital memory element as either a 1 or a 0 as part of a bitmask. After the image data is truncated, the truncated image data along with the bitmask can be sent to a processor. The processor can perform a decompression process by utilizing the bitmask and truncated image data to generate an image representative of the scene captured by the focal plane from which truncated image data is generated. In many embodiments, the decompression process can configure the processor to use a bitmask to decompress the truncated image data to produce a complete set of image data corresponding to every pixel location within a focal plane.


Systems and methods for compressing image data captured by pixels within a focal plane of an imager array in accordance with embodiments of the invention are discussed further below.


System Architecture


Array cameras in accordance with many embodiments of the invention can include a camera module, compression module and a processor. The camera module can include an array of cameras. A camera module can also include an imager array, which is a sensor that includes an array of focal planes. Each focal plane includes an array of pixels used to capture an image formed on the focal plane by a lens stack. The focal plane can be formed of, but is not limited to, traditional CIS (CMOS Image Sensor), CCD (charge-coupled device), high dynamic range sensor elements, multispectral sensor elements and various alternatives thereof. In many embodiments, the pixels of each focal plane have similar physical properties and receive light through the same lens stack. Furthermore, the pixels in each focal plane may be associated with the same color filter. In a number of embodiments, at least one of the focal planes includes a Bayer-pattern filter. In several embodiments, the focal planes are independently controlled. In other embodiments, the operation of the focal planes in the imager array is controlled via a single set of controls.


An array camera in accordance with an embodiment of the invention is illustrated in FIG. 1A. The array camera 100 includes a camera module 102 that is configured to transmit image data to a processor 108. The processor 108 is connected to a memory 110. The camera module 102 includes an array of cameras 104. The cameras 104 in the camera module 102 are formed from the combination of a lens stack and a focal plane. The camera module 102 can include an optic array of lens stacks and an imager array of focal planes. These multiple cameras 104 may be active or inactive at any given time. Array cameras are discussed in U.S. patent application Ser. No. 13/106,797 entitled “Architectures for Imager Arrays and Array cameras” and U.S. patent application Ser. No. 12/952,106 entitled “Capturing and Processing of Images using Monolithic Camera Array with Heterogeneous Imagers”, the disclosures of both applications are hereby incorporated by reference in their entirety. The image data captured by these multiple cameras is sent from the focal planes of each camera to a processor via a compression module 106 resident in the camera module. In certain embodiments, the compression module is integrated within the imager array. The compression module 106 compresses the image data by truncating the image data and generating a bitmask describing the truncation of the image data, thereby compressing image data sent from each focal plane to the processor 108.


A single focal plane of the array camera of FIG. 1A in accordance with an embodiment of the invention is illustrated in FIG. 1B. The focal plane 150 is composed of a square array of pixels 152. Each pixel of the focal plane can generate image data as a pixel value based upon the intensity of light incident upon the pixel and the exposure and analog gain settings of the pixel. That image data can be compressed using a compression module that truncates redundant image data.


In many embodiments, the array camera captures images using a plurality of cameras, which can have different imaging characteristics. The array camera can separately control each of the cameras to obtain enhanced image capture and/or to enhance processes such as (but not limited to) super-resolution processes that may be applied to the captured images. For example, each pixel of a focal plane can capture different wavelengths of light, or can capture the intensity of light, varying exposure times, start times, or end times relative to each pixel of a different focal plane. Once the array camera has commenced capturing image data using the pixels on the imager array, the focal planes can commence transmitting the image data captured using the pixels. The image data can be compressed using a compression module and the compressed image data transmitted to the processor.


Although specific array camera system architectures are discussed above, any of a variety of system architectures for array cameras can be utilized as appropriate to the requirements of a specific application in accordance with embodiments of the invention. Camera modules in accordance with embodiments of the invention are discussed further below.


Camera Modules


Camera modules in accordance with embodiments of the invention can be constructed from an imager array and an optic array. A camera module in accordance with an embodiment of the invention is illustrated in FIG. 2. The camera module 200 includes an imager array 230 including an array of focal planes 240 along with a corresponding optic array 210 including an array of lens stacks 220. Within the array of lens stacks, each lens stack 220 creates an optical channel that forms an image of the scene on an array of light sensitive pixels within a corresponding focal plane 240. Each pairing of a lens stack 220 and focal plane 240 forms a single camera 104 within the camera module. Each pixel within a focal plane 240 of a camera 104 generates image data that can be sent from the camera 104 to the processor 108. In many embodiments, the lens stack within each optical channel is configured so that pixels of each focal plane 240 sample the same object space or region within the scene. In several embodiments, the lens stacks are configured so that the pixels that sample the same object space do so with sub-pixel offsets to provide sampling diversity that can be utilized to recover increased resolution through the use of super-resolution processes.


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


Although specific array camera modules are described above, any of a variety of techniques can be utilized to construct array cameras in accordance with embodiments of the invention. Imager arrays that can be utilized in the construction of monolithic array camera modules in accordance with embodiments of the invention are discussed further below.


Imager Arrays


An imager array in which the image capture settings of a plurality of focal planes can be independently configured in accordance with an embodiment of the invention is illustrated in FIG. 3. The imager array 300 includes a focal plane array core 302 that includes an array of focal planes 304 and all analog signal processing, pixel level control logic, signaling, and analog-to-digital conversion (ADC) circuitry. The imager array also includes focal plane timing and control circuitry 306 that is responsible for controlling the capture of image data using the pixels. In a number of embodiments, the focal plane timing and control circuitry utilizes reset and read-out signals to control the integration time of the pixels. In other embodiments, any of a variety of techniques can be utilized to control integration time of pixels and/or to capture image data using pixels. In many embodiments, the focal plane timing and control circuitry 306 provides flexibility of image data capture control, which enables features including (but not limited to) high dynamic range imaging, high speed video, and electronic image stabilization. In various embodiments, the imager array includes power management and bias generation circuitry 308. The power management and bias generation circuitry 308 provides current and voltage references to analog circuitry such as the reference voltages against which an ADC would measure the signal to be converted against. In many embodiments, the power management and bias circuitry also includes logic that turns off the current/voltage references to certain circuits when they are not in use for power saving reasons. In several embodiments, the imager array includes dark current and fixed pattern noise (FPN) correction circuitry 310 that increases the consistency of the black level of the image data captured by the imager array and can reduce the appearance of row temporal noise and column fixed pattern noise. In several embodiments, each focal plane includes reference pixels for the purpose of calibrating the dark current and FPN of the focal plane and the control circuitry can keep the reference pixels active when the rest of the pixels of the focal plane are powered down in order to increase the speed with which the imager array can be powered up by reducing the need for calibration of dark current and FPN.


In many embodiments, a single self-contained chip imager includes focal plane framing circuitry 312 that packages the data captured from the focal planes into a container file and can prepare the captured image data for transmission. In several embodiments, the focal plane framing circuitry includes information identifying the focal plane and/or group of pixels from which the captured image data originated. In a number of embodiments, the imager array also includes an interface for transmission of captured image data to external devices. Image data provided to the interface can be directed through a compression module 314 that compresses the image data using any of a variety of compression processes discussed in more detail below. In the illustrated embodiment, the interface is a MIPI CSI 2 output interface (as specified by the non-profit MIPI Alliance, Inc.) supporting four lanes that can support read-out of video at 30 fps from the imager array and incorporating data output interface circuitry 316, interface control circuitry 318. Typically, the bandwidth of each lane is optimized for the total number of pixels in the imager array and the desired frame rate. The use of various interfaces including the MIPI CSI 2 interface to transmit image data captured by an array of imagers within an imager array to an external device in accordance with embodiments of the invention is described in U.S. Pat. No. 8,305,456, entitled “Systems and Methods for Transmitting Array Camera Data”, issued Nov. 6, 2012, the disclosure of which is incorporated by reference herein in its entirety.


Although specific components of an imager array architecture are discussed above with respect to FIG. 3, any of a variety of imager arrays can be constructed in accordance with embodiments of the invention that enable the capture of images of a scene at a plurality of focal planes in accordance with embodiments of the invention. The compression and transmission of image data captured by a plurality of focal planes in an imager array and a bitmask that enables a processor to construct reconstruct the image data from the compressed image data in accordance with embodiments of the invention is discussed further below.


Image Data Compression


Image data in accordance with many embodiments of the invention can be compressed utilizing a compression module that truncates image data from a row of pixels. The image data can be truncated according to digital logic resident in the compression module that detects for redundancies among image data read out from pixels within rows (or columns) of pixels, such as but not limited to truncating redundant pixel values from neighboring pixels along a row (or column) of pixels such that less than all of the redundant values from neighboring pixels are transmitted from a focal plane to a processor. A bitmap can also be generated by the compression module and sent to the processor that receives the compressed image data such that the processor using a decompression process can reconstruct the uncompressed image data.


A truncation rule can be set in accordance with any constraint to determine whether to truncate or transmit pixel values. In certain embodiments, a truncation rule can require that a certain amount of image data must be omitted to achieve a particular output bandwidth constraint. A particular embodiment can dictate that at most image data from 75% of the pixels of a focal plane can be transmitted to achieve a bandwidth usage target. In other embodiments, any of a variety of criteria can be utilized to control the compression process to achieve a target bandwidth.


In many embodiments, image data can be compressed by truncating redundant or unwanted image data such that only image data from pixels that contain important information is transmitted to a processor. Compression modules can store image data as pixel values in a shift register from pixels across a row of pixels temporarily, allowing for digital logic to quickly analyze image data relative to neighboring pixels to determine whether pixel values from certain pixels should be truncated. The shift register can be implemented as a cascade of flip flops that share the same clock signal. Shifting at a transition of the clock signal causes the image data stored in the shift register to shift by one position, enabling the compression module to load in and shift out pixel values one pixel value at a time. This also enables simultaneous access to pixel intensity values from adjacent pixels. In many embodiments, intensity values from adjacent pixels from a row or column of pixels are analyzed. In many embodiments, a compression module with a shift register 3 words deep can store pixel values from three different pixels, thereby providing access to values from pixels in the present clock cycle's pixel, N, as well as pixels from the previous two consecutive clock cycles N−1 and N−2. Digital logic resident in the compression module can then analyze the pixel value stored at the clock cycle N−1 for comparison to pixel value stored at clock cycle N and N−2.


A number of redundancy criteria can be applied by the digital logic so as to determine if the pixel value at clock cycle N−1 is worth transmitting. In many embodiments, a truncation rule can be utilized to determine a threshold of redundancy beyond which pixel values are truncated. In certain embodiments, the truncation rule can utilize the following relationship:

K=(2*N−1)−N−N−2

Where only when K>threshold, is the pixel value at clock cycle N−1 transmitted. This configurable threshold could be set to, for example, the noise floor of the focal plane or alternatively be additionally made a function of the intensity value of pixel N−1 such that both the noise floor and photon shot noise can be used to set the threshold. If a particular pixel has K value less than the threshold, the image data from the pixel can be considered to be of no significance as being indistinguishable from noise. In other embodiments, any of a variety of criterion can be utilized to determine whether to truncate a pixel based upon the intensity values of one or more adjacent pixels in a row or column of pixels read out from a focal plane.


In a number of embodiments, a compression module can employ digital logic to determine whether compression of image data by truncation is necessary by determining if there is a compression advantage. In numerous embodiments, a compression advantage determines if transmitting compressed image data is advantageous relative to transmitting non compressed image data. This type of compression advantage can determine whether the combination of the truncated image data and the bitmask would require less bandwidth to transmit than merely uncompressed image data. In certain embodiments, a compression advantage is present when the number of pixel values truncated multiplied by the number of bits required to store a pixel value is larger than the number of bits in the bitmask.


In several embodiments, a bitmask can be created to record truncation of image data to be used by a processor using a decompression process to generate an image representative of the scene captured by the focal plane from which truncated image data is generated. In certain embodiments, a bitmask represents whether image data as pixel values from particular pixels is truncated as either a 1 or a 0 associated with the particular pixel in the bitmask. Thereby, in many embodiments, image data from a row of 1000 pixels would require memory of 1000 bits in size to store information in a bitmask. The location of bits within the bitmask indicates the location of each pixel in which image is truncated.


A focal plane can utilize one or multiple bitmasks in transmitting compressed image data. A bitmask can be inserted into a stream of truncated image data sent to a processor periodically, such as but not limited to being inserted with each row of truncated image data, or once upon successful transmission of image data associated with an entire focal plane.


A process for image data compression in accordance with an embodiment of the invention is illustrated in FIG. 4. The compression process 400 includes receiving (402) image data as pixel values from pixels in a row of pixels of a focal plane at a compression module. The image data can be analyzed (404) by digital logic resident on the compression module for compression. The analysis can be made in accordance with a truncation rule, such as (but not limited) to the various truncation rules described above. The compression can be achieved by truncating (406) image data from a row of pixels in accordance with the analysis. A bit mask can be generated (408) that notates the truncation of image data. The compressed image data, as truncated image data and a bitmask, can be sent (410) to a processor for reconstruction of a scene captured by the focal plane using a decompression process. In many embodiments, the decompression process can configure the processor to use the truncated image data to generate pixel values associated with pixel locations (as represented within image data used to generate an image) found using the bitmask to be missing an associated pixel value due to truncation.


A decompression process in accordance with an embodiment of the invention is illustrated in FIG. 5. The decompression process 500 includes receiving (502) compressed image data from a row (or column of pixels) and extracting a bit mask that can be used to decompress (504) truncated image data within the compressed image data based upon a known truncation rule, such as (but not limited to) the truncation rules described above. In several embodiments, the process (optionally) involves encoding the decompressed image data using an image encoding format such as (but not limited to) the lossless JPEG image encoding formats specified by the Joint Photographic Experts Group and/or the image compression format specified in U.S. Provisional Patent Application Ser. No. 61/767,520 entitled “Systems and Methods for Generating Captured Light Field Image Data Using Captured Light Fields”, filed Feb. 21, 2013, the disclosure of which is incorporated by reference herein in its entirety. The (encoded) image data can then be stored for use in additional processing.


Although specific embodiments of array camera image data compression are discussed above, many other implementations of image data compression are possible in accordance with various embodiments of the invention. While the above description contains many specific embodiments of the invention, these should not be construed as limitations on the scope of the invention, but rather as an example of one embodiment thereof.


Packetizing Image Data Captured by Multiple Focal Planes


In several embodiments, image data from a plurality of focal planes can be packetized by inserting the image data and/or additional data describing the image data into a packet in such a way that a processor can reconstruct images of a scene from the received image data. A conceptual illustration of a packet including image data and additional data describing the image data transmitted by an imager array in accordance with an embodiment of the invention is illustrated in FIG. 6. The packet 602 includes a packet header 604 and a packet footer 606. The packet 602 also includes a number of lines 608 of image data, where each line 608 of image data includes a line header 610 and a line footer 612. In many embodiments, the packet header 604 and/or the line header 610 contain additional data that describes the image data in such a way that a device receiving the packet can reconstruct a plurality of images using image data including the lines of image data contained within the packet. The number of lines of image data included within the packet typically depends upon the requirements of a specific application. In many embodiments, the packet can contain all of the lines of image data for a single captured light field. The term light field can be used to describe a number of two dimensional images that are captured of a scene from different perspectives. In other embodiments, the lines of image data of a captured light field can be divided and sent in multiple packets. In many embodiments, image data captured by individual pixels or groups of pixels from within a line of pixels from different focal planes can be interleaved within a packet of image data.


In a number of embodiments, the packet header 604 contains embedded data. In many embodiments, the embedded data describes the camera module from which image data is generated in such a way that a processor can determine the structure of the image data in the packet and reconstruct images from the data received from the camera module. In several embodiments, a packet header 604 includes embedded data such as (but not limited to) the number of focal planes in an imager array, the timing of the image capture per focal plane, the identity of the particular focal planes being read out, the total number of pixels in a focal plane, the resolution of an image taken by a focal plane, the timing of the pixel read outs and the gain for the focal plane. As discussed below, the embedded data described above need not be included in a packet header and some or all of the information can be transmitted accompanying image data in different ways including but not limited to locating the additional data elsewhere in the packet and/or transmitting the additional data in a separate packet. Embedded data describing imaging data in accordance with embodiments of the invention is discussed further below.


In the illustrated embodiment, the lines 608 of image data include line headers 610. The line header identifies the focal plane or focal planes and pixels in the imager array that captured the image data contained within the line of image data. A processor can utilize the line header to identify the specific image data contained within the line 610 of image data. In various embodiments, a line header 610 includes information such as (but not limited to) the identify of the focal plane that captured the image data within the line and/or the identity of the specific pixels(s) or group of pixels used to capture the image data contained within the line of data, and a timestamp. Stated another way, a line of image data within a packet formatted in accordance with embodiments of the invention need not correspond to image data captured using a single line of pixels in a single focal plane. Indeed, packets of image data in accordance with embodiments of the invention can include lines of image data containing image data captured by different lines of pixels and/or from different focal planes. Inclusion of the additional data describing the line of image data in the line header allows a processor to receive and process image data from multiple images multiplexed into a single packet or stream of packets. Different types of embedded data that can be included in line headers (or elsewhere) in accordance with embodiments of the invention are discussed further below.


Each line 608 of image data can include a line footer 612 to indicate that the line of image data 608 associated with the preceding line header 610 has ended. Also, each packet 602 can include a packet footer 606 to indicate that the image data associated with the previous packet header 604 has ended. In many embodiments, the imager array is configured to generate multiple packets 602 to contain the image data captured by the focal planes and each packet includes multiple lines of image data.


Due to the manner in which image data is captured by different sets of pixels in different focal planes as data is transmitted by the imager array, the processor typically cannot predict the order in which it will receive image data from the imager array. In many embodiments, the processor has no knowledge of the focal plane or focal planes that captured the image data contained within a line of image data without reference to the packet header and/or the line header for the line of image data. However, in other embodiments the imager array imposes constraints with respect to the order in which image data is captured by specific focal planes (see for example the discussion below with respect to FIGS. 8a-8c) and a processor can rely upon the predetermined order of image data capture to reconstruct the image data. While imposing constraints on the order in which image data is captured can reduce the flexibility of the image array with respect to the manner in which image data is captured from different focal planes, the predictable manner in which image data is received from the imager array can result in the reduction in the amount of additional data transmitted in conjunction with the image data by removing information that identifies the focal plane and/or pixels that captured the image data. In many embodiments, the manner in which the imager array is constrained to capture image data enables the packet header, the packet footer, the line header and/or the line footer illustrated in FIG. 6 to be eliminated.


Although the inclusion of specific pieces of information within packet headers and/or line headers is described above, any information that enables the reconstruction of multiple images from image data multiplexed into a single packet or stream of packets can be incorporated into a packet of image data in accordance with embodiments of the invention. Transmission of image data compatible with the MIPI interface format is discussed further below.


Image Data Transmission Compatible with the MIPI Interface Format


In several embodiments, imager arrays transmit image data and additional data describing the image data in a manner that is compatible with an existing interface format for the transmission of image data by a conventional camera including a single focal plane. A conceptual illustration of image data and additional data describing the image data transmitted as packets compatible with the MIPI CSI-2 standard interface format (MIPI interface format) in accordance with an embodiment of the invention is illustrated in FIG. 7. The conceptual illustration can be read as involving transmissions from left to right in the X direction 720 and from top to bottom in the Y direction 722. The transmission begins with a MIPI frame blanking interval 702. A MIPI frame start (MFS) 710 indicator is then sent by the imager array, followed by a portion of the MIPI header 712. A packet of data generated in accordance with embodiments of the invention is inserted within the standard MIPI container as embedded data. Accordingly, the first line of data within the MIPI container can include a packet header 724 containing information concerning the focal planes that generated the image data (see discussion above).


The transmission of the first line of the MIPI container is completed by the transmission of a MIPI footer 706. There is a pause during the MIPI line blanking interval 708, and then the next portion of the MIPI header 712 is transmitted. The next line of the MIPI container includes a line of image data 714. In embodiments where the order in which the lines of image data transmitted by the imager array is not predetermined, the line of image data can be preceded by a line header and followed by a line footer. In embodiments where the lines of image data are transmitted in a predetermined order (see for example the discussion of FIGS. 8a-8c), a line header and/or line footer may not be utilized.


The process of transmitting a MIPI footer, pausing during a MIPI line blanking interval, transmitting a portion of the MIPI header, and transmitting lines of image data within the MIPI container continues until all the lines of image data in the packet are transmitted. In several embodiments, an embedded packet footer is transmitted in the MIPI container to indicate that the transmission of the packet is complete. Following the transmission of the packet, the transmission of the MIPI container is completed by transmitting a MIPI footer 706 and a MIPI frame end 716. Although the packet illustrated in FIG. 7 involves transmitting one line of image data between line blanking intervals of the MIPI container, in many embodiments the packet header and the lines of image data do not correspond with the line blanking intervals of the MIPI container. Stated another way, a single line blanking interval of the MIPI container can contain image data from two or more lines of image data. Accordingly, the line headers and/or line footers are utilized to identify the individual lines of image data within the container.


As can readily be appreciated, the process illustrated in FIG. 7 involves formatting a packet of data including image data and additional data describing the image data generated in accordance with embodiments of the invention within a conventional MIPI container. In this way, an imager array can utilize an interface standard developed to enable the transmission of image data captured by a single focal plane to enable transmission of a packet of data containing image data captured by a plurality of focal planes (i.e. a light field). In other embodiments, similar processes can be utilized to transmit packets formatted in the manner outlined above using other containers and/or interface formats including (but not limited to) a CameraLink interface format, a USB interface format, or a Firewire interface format.

Claims
  • 1. A system comprising: compression circuitry including at least one input via which the compression circuitry is configured to: receive image data captured by a plurality of cameras having different viewpoints, wherein the image data is in sequentially linear groups of pixel data, and wherein the pixel data are from corresponding pixel locations in a plurality of different images captured by the plurality of cameras;wherein the compression circuitry is further configured to: identify corresponding pixel locations in sequentially linear groups of pixel data received from different cameras in the plurality of cameras;identify redundant pixel data in at least one sequentially linear group of pixel data by determining similarity between the pixel data, wherein identifying whether a given pixel data in the sequentially linear groups of pixel data is redundant comprises: calculating a multiple of the given pixel data in the sequentially linear groups of pixel data,calculating a difference between the multiple of the given pixel data and at least one pixel data neighboring the given pixel data, andwhen the calculated difference is less than a threshold value, identifying the given pixel data as a redundant pixel data;truncate pixel data identified as redundant from the at least one sequentially linear group of pixel data to form compressed image data;multiplex compressed image data with pixel data from the identified corresponding pixel locations in sequentially linear groups of pixel data received from different cameras in the plurality of cameras; andtransmit the multiplexed compressed image data and pixel data from the compression circuitry to a processing system via interface circuitry.
  • 2. A method for compressing image data captured by a plurality of cameras comprising: receiving image data captured by a plurality of cameras having different viewpoints using a compression circuitry, wherein the image data is in sequentially linear groups of pixel data, and wherein the pixel data are from corresponding pixel locations in a plurality of different images captured by the plurality of cameras;identifying corresponding pixel locations in sequentially linear group of pixel data received from different cameras in the plurality of cameras using the compression circuitry, wherein identifying whether a given pixel data in the sequentially linear groups of pixel data is redundant comprises: calculating a multiple of the given pixel data in the sequentially linear groups of pixel data,calculating a difference between the multiple of the given pixel data and at least one pixel data neighboring the given pixel data, andwhen the calculated difference is less than a threshold value, identifying the given pixel data as a redundant pixel data;identifying redundant pixel data in at least one sequentially linear group of pixel data by determining similarity between the pixel data using the compression circuitry;truncating pixel data identified as redundant from the at least one sequentially linear group of pixel data to form compressed image data using the compression circuitry;multiplexing compressed image data with pixel data from the identified corresponding pixel locations in sequentially linear groups of pixel data received from different cameras in the plurality of cameras using the compression circuitry; andtransmitting the multiplexed compressed image data and pixel data from the compression circuitry to a processing system via interface circuitry.
  • 3. The method of claim 2, wherein the threshold value is a noise floor below which pixel data is indistinguishable from noise.
  • 4. The method of claim 2, wherein: the given pixel data is pixel data N−1,the multiple is 2,the at least one pixel data neighboring the given pixel data include pixel data N and pixel data N−2, andthe difference K is calculated according to the following truncation rule: K=(2*N−1)−N−N−2.
  • 5. The method of claim 2, wherein the multiplexed compressed image data and pixel data is transmitted in a stream of packets.
  • 6. The method of claim 5, wherein the stream of packets comprises a header describing how image data from the plurality of cameras is multiplexed within the stream of packets.
  • 7. The method of claim 2 further comprising storing the received image data in a shift register prior to identifying redundant pixel data in at least one sequentially linear group of pixel data.
  • 8. The system of claim 1, wherein the threshold value is a noise floor below which pixel data is indistinguishable from noise.
  • 9. The system of claim 1, wherein: given pixel data is pixel data N−1,the multiple is 2,the at least one pixel data neighboring the given pixel data include pixel data N and pixel data N−2, andthe difference K is calculated according to the following truncation rule: K=(2*N−1)−N−N−2.
  • 10. The system of claim 1, wherein the compression circuitry is further configured to transmit the multiplexed compressed image data and pixel data in a stream of packets.
  • 11. The system of claim 10, wherein the stream of packets comprises a header describing how image data from the plurality of cameras is multiplexed within the stream of packets.
  • 12. The system of claim 1, wherein the compression circuitry is further configured to store the received image data in a shift register prior to identifying redundant pixel data in at least one sequentially linear group of pixel data.
CROSS-REFERENCE TO RELATED APPLICATIONS

The current application claims priority to U.S. Provisional Patent Application No. 61/776,751 entitled “System and Methods for Image Data Compression,” filed Mar. 11, 2013, the disclosure of which is incorporated herein by reference.

US Referenced Citations (493)
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 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
6124974 Burger Sep 2000 A
6137100 Fossum et al. Oct 2000 A
6137535 Meyers Oct 2000 A
6141048 Meyers Oct 2000 A
6163414 Kikuchi et al. Dec 2000 A
6175379 Uomori et al. Jan 2001 B1
6239909 Hayashi et al. May 2001 B1
6358862 Ireland et al. Mar 2002 B1
6443579 Myers et al. Sep 2002 B1
6477260 Shimomura Nov 2002 B1
6525302 Dowski, Jr. et al. Feb 2003 B2
6563537 Kawamura et al. May 2003 B1
6603513 Berezin Aug 2003 B1
6611289 Yu 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
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
6927922 George et al. Aug 2005 B2
6958862 Joseph Oct 2005 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
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
7738013 Galambos et al. Jun 2010 B2
7782364 Smith Aug 2010 B2
7840067 Shen et al. Nov 2010 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
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
8169486 Corcoran et al. May 2012 B2
8180145 Wu 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
8254668 Mashitani et al. Aug 2012 B2
8280194 Wong et al. Oct 2012 B2
8289409 Chang Oct 2012 B2
8289440 Pitts et al. Oct 2012 B2
8294099 Blackwell, Jr. Oct 2012 B2
8305456 McMahon Nov 2012 B1
8345144 Georgiev et al. Jan 2013 B1
8360574 Ishak et al. Jan 2013 B2
8406562 Bassi et al. Mar 2013 B2
8446492 Nakano et al. May 2013 B2
8456517 Mor et al. Jun 2013 B2
8493496 Freedman et al. Jul 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
8648918 Kauker et al. Feb 2014 B2
8655052 Spooner et al. Feb 2014 B2
8682107 Yoon et al. Mar 2014 B2
8687087 Pertsel et al. Apr 2014 B2
8692893 McMahon Apr 2014 B2
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
8866920 Venkataraman et al. 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
9019426 Han et al. Apr 2015 B2
9025894 Venkataraman et al. May 2015 B2
9025895 Venkataraman et al. May 2015 B2
9030528 Pesach 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
9055233 Venkataraman et al. Jun 2015 B2
9060124 Venkataraman et al. Jun 2015 B2
9094661 Venkataraman et al. Jul 2015 B2
9123117 Ciurea et al. Sep 2015 B2
9123118 Ciurea et al. Sep 2015 B2
9124815 Venkataraman et al. Sep 2015 B2
9129183 Venkataraman et al. Sep 2015 B2
9129377 Ciurea et al. Sep 2015 B2
9143711 McMahon Sep 2015 B2
9147254 Ciurea et al. Sep 2015 B2
9188765 Venkataraman et al. Nov 2015 B2
9191580 Venkataraman et al. Nov 2015 B2
9197821 McMahon Nov 2015 B2
9235898 Venkataraman et al. Jan 2016 B2
9235900 Ciurea et al. Jan 2016 B2
9240049 Ciurea et al. Jan 2016 B2
20010005225 Clark et al. Jun 2001 A1
20010019621 Hanna et al. Sep 2001 A1
20020012056 Trevino Jan 2002 A1
20020027608 Johnson Mar 2002 A1
20020039438 Mori et al. Apr 2002 A1
20020057845 Fossum May 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 Aug 2002 A1
20020113867 Takigawa et al. Aug 2002 A1
20020113888 Sonoda et al. Aug 2002 A1
20020163054 Suda Nov 2002 A1
20020167537 Trajkovic Nov 2002 A1
20020177054 Saitoh et al. Nov 2002 A1
20030086079 Barth et al. May 2003 A1
20030124763 Fan et al. 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 Jan 2004 A1
20040047274 Amanai Mar 2004 A1
20040050104 Ghosh et al. Mar 2004 A1
20040056966 Schechner et al. Mar 2004 A1
20040066454 Otani et al. Apr 2004 A1
20040096119 Williams May 2004 A1
20040100570 Shizukuishi May 2004 A1
20040105021 Hu et al. Jun 2004 A1
20040114807 Lelescu et al. Jun 2004 A1
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
20040207836 Chhibber et al. Oct 2004 A1
20040213449 Safaee-Rad et al. Oct 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
20050007461 Chou 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
20050132098 Sonoda et al. Jun 2005 A1
20050134698 Schroeder 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 Oct 2005 A1
20050224843 Boemler Oct 2005 A1
20050225654 Feldman et al. Oct 2005 A1
20050286612 Takanashi Dec 2005 A1
20060002635 Nestares et al. Jan 2006 A1
20060007331 Izumi 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
20060034531 Poon et al. Feb 2006 A1
20060038891 Okutomi et al. Feb 2006 A1
20060039611 Rother 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
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
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
20060210186 Berkner Sep 2006 A1
20060214085 Olsen et al. 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
20070008575 Yu 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 Oct 2007 A1
20070257184 Olsen et al. Nov 2007 A1
20070258006 Olsen et al. Nov 2007 A1
20070258706 Raskar et al. Nov 2007 A1
20070268374 Robinson Nov 2007 A1
20070296832 Ota et al. Dec 2007 A1
20070296835 Olsen 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
20080056302 Erdal 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
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
20080152296 Oh et al. Jun 2008 A1
20080156991 Hu et al. Jul 2008 A1
20080158259 Kempf et al. Jul 2008 A1
20080158375 Kakkori et al. Jul 2008 A1
20080174670 Olsen et al. Jul 2008 A1
20080187305 Raskar 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
20080291295 Kato Nov 2008 A1
20080310501 Ward 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
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
20090256947 Ciurea et al. Oct 2009 A1
20090263017 Tanbakuchi Oct 2009 A1
20090268192 Koenck et al. Oct 2009 A1
20090268970 Babacan et al. Oct 2009 A1
20090268983 Stone 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
20100074532 Gordon et al. Mar 2010 A1
20100085425 Tan Apr 2010 A1
20100086227 Sun 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
20100118127 Nam et al. May 2010 A1
20100142839 Lakus-becker Jun 2010 A1
20100157073 Kondo et al. Jun 2010 A1
20100177411 Hegde et al. Jul 2010 A1
20100195716 Klein Gunnewiek 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
20100223237 Mishra et al. Sep 2010 A1
20100231285 Boomer et al. Sep 2010 A1
20100244165 Lake et al. Sep 2010 A1
20100265385 Knight et al. Oct 2010 A1
20100281070 Chan et al. Nov 2010 A1
20100302423 Adams, Jr. et al. Dec 2010 A1
20110001037 Tewinkle Jan 2011 A1
20110019243 Constant, Jr. et al. 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
20110153248 Gu et al. Jun 2011 A1
20110206291 Kashani et al. Aug 2011 A1
20110211824 Georgiev et al. Sep 2011 A1
20110221599 Högasten Sep 2011 A1
20110221658 Haddick et al. Sep 2011 A1
20110234841 Akeley et al. Sep 2011 A1
20110241234 Duparre Oct 2011 A1
20110255745 Hodder et al. Oct 2011 A1
20110261993 Weiming et al. Oct 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
20110285910 Bamji et al. Nov 2011 A1
20110300929 Tardif et al. Dec 2011 A1
20110310980 Mathew Dec 2011 A1
20110317766 Lim et al. Dec 2011 A1
20120012748 Pain et al. Jan 2012 A1
20120023456 Sun et al. Jan 2012 A1
20120026342 Yu et al. Feb 2012 A1
20120039525 Tian et al. Feb 2012 A1
20120044249 Mashitani 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
20120147205 Lelescu et al. Jun 2012 A1
20120153153 Chang et al. Jun 2012 A1
20120155830 Sasaki et al. Jun 2012 A1
20120176479 Mayhew et al. Jul 2012 A1
20120188420 Black et al. Jul 2012 A1
20120198677 Duparre Aug 2012 A1
20120200734 Tang Aug 2012 A1
20120219236 Ali et al. Aug 2012 A1
20120224083 Jovanovski et al. Sep 2012 A1
20120249550 Akeley et al. Oct 2012 A1
20120249836 Ali et al. Oct 2012 A1
20120262601 Choi Oct 2012 A1
20120262607 Shimura et al. Oct 2012 A1
20120268574 Gidon 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
20130003184 Duparre Jan 2013 A1
20130010073 Do 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
20130070060 Chatterjee 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
20130128087 Georgiev et al. May 2013 A1
20130147979 McMahon et al. Jun 2013 A1
20130215108 McMahon et al. Aug 2013 A1
20130229540 Farina et al. Sep 2013 A1
20130230237 Schlosser et al. Sep 2013 A1
20130259317 Gaddy Oct 2013 A1
20130265459 Duparre et al. Oct 2013 A1
20130293760 Nisenzon et al. Nov 2013 A1
20140076336 Clayton et al. Mar 2014 A1
20140079336 Venkataraman et al. Mar 2014 A1
20140092281 Nisenzon et al. Apr 2014 A1
20140098267 Tian et al. Apr 2014 A1
20140118493 Sali et al. May 2014 A1
20140118584 Lee May 2014 A1
20140132810 McMahon May 2014 A1
20140176592 Wilburn et al. Jun 2014 A1
20140218546 Mcmahon Aug 2014 A1
20140232822 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
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
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
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
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
20150312455 Venkataraman et al. Oct 2015 A1
Foreign Referenced Citations (54)
Number Date Country
1839394 Sep 2006 CN
840502 May 1998 EP
2336816 Jun 2011 EP
59-025483 Sep 1984 JP
64-037177 Jul 1989 JP
02-285772 Nov 1990 JP
2000209503 Jul 2000 JP
2002252338 Sep 2002 JP
2003163938 Jun 2003 JP
2004221585 Aug 2004 JP
2005295381 Oct 2005 JP
2006033493 Feb 2006 JP
2007520107 Jul 2007 JP
2008507874 Mar 2008 JP
2008258885 Oct 2008 JP
2011109484 Jun 2011 JP
2013526801 Jun 2013 JP
1020110097647 Aug 2011 KR
2007083579 Jul 2007 WO
2008108926 Sep 2008 WO
2009151903 Dec 2009 WO
2011063347 May 2011 WO
2011116203 Sep 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
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
2014032020 May 2014 WO
2014078443 May 2014 WO
2014130849 Aug 2014 WO
2014138695 Sep 2014 WO
2014138697 Sep 2014 WO
2014144157 Sep 2014 WO
2014145856 Sep 2014 WO
2014150856 Sep 2014 WO
2014159721 Oct 2014 WO
2014159779 Oct 2014 WO
2014160142 Oct 2014 WO
2014164550 Oct 2014 WO
2014165244 Oct 2014 WO
2015048694 Apr 2015 WO
Non-Patent Literature Citations (163)
Entry
US 8,957,977, 02/2015, Venkataraman et al. (withdrawn)
US 8,964,053, 02/2015, Venkataraman et al. (withdrawn)
US 8,965,058, 02/2015, Venkataraman et al. (withdrawn)
US 9,014,491, 04/2015, Venkataraman et al. (withdrawn)
Extended European Search Report for European Application EP12835041.0, Report Completed Jan. 28, 2015, Mailed Feb. 4, 2015, 6 Pgs.
International Search Report and Written Opinion for International Application PCT/US2014/024903 report completed Jun. 12, 2014, Mailed Jun. 27, 2014, 13 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, Report completed May 23, 2014, Mailed Jun. 10, 2014, 12 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/018116, report completed May 13, 2014, Mailed Jun. 2, 2014, 12 Pgs.
International Search Report and Written Opinion for International Application PCT/US2014/022118, report completed Jun. 9, 2014, Mailed Jun. 25, 2014, 5 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/024407, report completed Jun. 11, 2014, Mailed Jul. 8, 2014, 9 Pgs.
International Search Report and Written Opinion for International Application PCT/US2014/025100, report completed Jul. 7, 2014, Mailed Aug. 7, 2014, 5 Pgs.
International Search Report and Written Opinion for International Application PCT/US2014/022123, report completed Jun. 9, 2014, Mailed Jun. 25, 2014, 5 pgs.
International Search Report and Written Opinion for International Application PCT/US2014/024947, report completed Jul. 8, 2014, Mailed Aug. 5, 2014, 8 Pgs.
International Search Report and Written Opinion for International Application PCT/US2014/030692, report completed Jul. 28, 2014, Mailed Aug. 27, 2014, 7 Pgs.
Chen et al., “Interactive deformation of Light Fields”, In Proceedings of SIGGRAPH (I3D) 2005, pp. 139-146.
Chen et al., “KNN Matting”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Sep. 2013, vol. 35, No. 9, pp. 2175-2188.
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.
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.
Joshi et al., “Synthetic Aperture Tracking: Tracking Through Occlusions”, I CCV IEEE 11th International Conference on Computer Vision, Oct. 2007, Retrieved from http:|/ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4409032&isnumber=4408819, on Retrieved Jul. 28, 2014, pp. 1-8.
Lai et al., “A Large-Scal Hierarchical Multi-View RGB-D Object Dataset”, source and date unknown, 8 pgs.
Levin et al., “A Closed Form Solution to Natural Image Matting”, Pattern Analysis and Machine Intelligence, Feb. 2008, vol. 30, 8 pgs.
Lo et al., “Stereoscopic 3D Copy & Paste”, ACM Transactions on Graphics, vol. 29, No. 6, Article 147, Dec. 2010, pp. 147:1-147:10.
Perwass et al., “Single Lens 3D-Camera with Extended Depth-of-Field”, printed from www.raytrix.de, 15 pgs.
Seitz et al., “Plenoptic Image Editing”, International Journal of Computer Vision 48, 2, pp. 1-29.
Tallon et al., “Upsampling and Denoising of Depth Maps via Joint-Segmentation”, 20th European Signal Processing Conference, Aug. 27-31, 2012, 5 pgs.
Zhang, Qiang 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.
International Search Report and Written Opinion for International Application No. PCT/US2014/066229, Search Completed Mar. 6, 2015, Mailed Mar. 19, 2015, 9 Pgs.
International Preliminary Report on Patentability for International Application No. PCT/US2013/056065, Report Issued Feb. 24, 2015, Mailed Mar. 5, 2015, 4 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2013/056502, Report Issued Feb. 24, 2015, Mailed Mar. 5, 2015, 7 Pgs.
Borman et al., “Simultaneous Multi-Frame MAP Super-Resolution Video Enhancement Using Spatio-Temporal Priors”, Image Processing, 1999, ICIP 99 Proceedings, vol. 3, pp. 469-473.
Borman et al., “Super-Resolution from Image Sequences—A Review”, Circuits & Systems, 1998, pp. 374-378.
Bose et al., “Superresolution and Noise Filtering Using Moving Least Squares”, IEEE Transactions on Image Processing, 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, Dec. 11, 2006, vol. 14, No. 25, pp. 12076-12084.
Bruckner et al., “Driving microoptical imaging systems towards miniature camera applications”, Proc. SPIE, Micro-Optics, 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.
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.
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.
Liu et al., “Virtual View Reconstruction Using Temporal Information”, 2012 IEEE International Conference on Multimedia and Expo, 2012, pp. 115-120.
Muehlebach, Michael, “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 back projection 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. Intel., 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.
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.
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.
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 Image Position, Size and Orientation Using First Order Properties”.
Wang, Yuhao, “Calculation of Image Position, Size and Orientation Using First Order Properties”.
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.
Zomet et al., “Robust Super-Resolution”, IEEE, 2001, pp. 1-6.
International Search Report and Written Opinion for International Application No. PCT/US13/46002, Search Completed Nov. 13, 2013, Mailed Nov. 29, 2013, 7 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/US13/59991, Search Completed Feb. 6, 2014, Mailed Feb. 26, 2014, 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 No. PCT/US2013/069932, International Filing Date Nov. 13, 2013, Search Completed Mar. 14, 2014, Mailed Apr. 14, 2014, 12 pgs.
International 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/US2012/044014, completed Oct. 12, 2012, 15 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/056151, completed Nov. 14, 2012, 10 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/059813, completed Dec. 17, 2012, 8 pgs.
International Search Report and Written Opinion for International Application PCT/US2012/37670, 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 pgs.
Bishop et al., “The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution”, IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2012, vol. 34, No. 5, 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 Non-Gaussian Image Models”, Proc. SPIE, 1998. 3459, 9 pgs.
Mitra et al., “Light Field Denoising, Light Field Superresolution and Stereo Camera Based Refocussing using a GMM Light Field Patch Prior”, Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference on Jun. 16-21, 2012, pp. 22-28.
International Preliminary Report on Patentability for International Application PCT/US2013/069932, Report issued May 19, 2015, Mailed May 28, 2015, 14 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/017766, Report issued Aug. 25, 2015, Mailed Sep. 3, 2015, 8 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/022118, Report issued Sep. 8, 2015, Mailed Sep. 17, 2015, 4pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/022123, Report issued Sep. 8, 2015, Mailed Sep. 17, 2015, 4 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/024407, Report issued Sep. 15, 2015, Mailed Sep. 24, 2015, 8 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/024903, Report issued Sep. 15, 2015, Mailed Sep. 24, 2015, 12 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/024947, Report issued Sep. 15, 2015, Mailed Sep. 24, 2015, 7 Pgs.
International Preliminary Report on Patentability for International Application PCT/US2014/025100, Report issued Sep. 15, 2015, Mailed Sep. 24, 2015, 4 Pgs.
International Search Report and Written Opinion for International Application No. PCT/US2015/019529, Search completed May 5, 2015, Mailed Jun 8, 2015, 10 Pgs.
International Search Report and Written Opinion for International Application PCT/US2010/057661, completed Mar. 9, 2011, 14 pgs.
Merkle, Philipp et al., “Adaptation and optimization of coding algorithms for mobile 3DTV”, Mobile3DTV Project No. 216503, Nov. 2008, 55 pgs.
Philips 3D Solutions, “3D Interface Specifications, White Paper”, Philips 3D Solutions retrieved from www.philips.com/3dsolutions, 29 pgs., Feb. 15, 2008.
Provisional Applications (1)
Number Date Country
61776751 Mar 2013 US