Systems and methods for synthesizing images from image data captured by an array camera using restricted depth of field depth maps in which depth estimation precision varies
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
H04N-013/02
H04N-005/225
G06T-007/00
출원번호
US-0207254
(2014-03-12)
등록번호
US-9519972
(2016-12-13)
발명자
/ 주소
Venkataraman, Kartik
Nisenzon, Semyon
Chatterjee, Priyam
Molina, Gabriel
출원인 / 주소
KIP PELI P1 LP
대리인 / 주소
KPPB LLP
인용정보
피인용 횟수 :
36인용 특허 :
146
초록▼
Systems and methods are described for generating restricted depth of field depth maps. In one embodiment, an image processing pipeline application configures a processor to: determine a desired focal plane distance and a range of distances corresponding to a restricted depth of field for an image re
Systems and methods are described for generating restricted depth of field depth maps. In one embodiment, an image processing pipeline application configures a processor to: determine a desired focal plane distance and a range of distances corresponding to a restricted depth of field for an image rendered from a reference viewpoint; generate a restricted depth of field depth map from the reference viewpoint using the set of images captured from different viewpoints, where depth estimation precision is higher for pixels with depth estimates within the range of distances corresponding to the restricted depth of field and lower for pixels with depth estimates outside of the range of distances corresponding to the restricted depth of field; and render a restricted depth of field image from the reference viewpoint using the set of images captured from different viewpoints and the restricted depth of field depth map.
대표청구항▼
1. An image processing system, comprising: a processor;memory containing a set of images captured from different viewpoints and an image processing pipeline application;wherein the image processing pipeline application configures the processor to: determine a range of distances corresponding to a re
1. An image processing system, comprising: a processor;memory containing a set of images captured from different viewpoints and an image processing pipeline application;wherein the image processing pipeline application configures the processor to: determine a range of distances corresponding to a restricted depth of field; andgenerate a restricted depth of field depth map from the reference viewpoint using the set of images captured from different viewpoints, where depth estimation precision is higher for pixels with depth estimates within the range of distances corresponding to the restricted depth of field and lower for pixels with depth estimates outside of the range of distances corresponding to the restricted depth of field;wherein the image processing pipeline application further configures the processor to generate a restricted depth of field depth map by: performing a disparity search with respect to a given pixel location using the set of images captured from different viewpoints;wherein the disparity search is performed using a greater density of depth samples within the range of distances corresponding to the restricted depth of field and a lower density of depth samples for distances outside the range of distances corresponding to the restricted depth of field. 2. The image processing system of claim 1, wherein the image processing pipeline application further configures the processor to automatically determine the range of distances corresponding to a restricted depth of field. 3. The image processing system of claim 2, wherein the image processing pipeline application further configures the processor to automatically determine the range of distances corresponding to the restricted depth of field by determining a distance to a surface of a scene object using the set of images captured from different viewpoints. 4. The image processing system of claim 3, wherein the image processing pipeline application further configures the processor to determine a distance to a surface of a scene object using the set of images captured from different viewpoints by: generating an initial depth map and a confidence map from at least a portion of the set of images captured from different viewpoints, where the confidence map indicates the reliability of pixel depth estimates in the initial depth map; anddetermining the depth of the surface of the scene object based upon at least one pixel depth estimate within the initial depth map marked as confident within the confidence map. 5. The image processing system of claim 3, wherein the image processing pipeline application further configures the processor to receive a user instruction identifying a surface of a scene object by: generating a preview image from the set of images captured from different viewpoints, where the preview image includes a user interface cue; andidentifying a surface of a scene object visible within the set of images captured from different viewpoints based upon the location of the user interface cue. 6. The image processing system of claim 3, wherein the image processing pipeline application further configures the processor to determine the range of distances corresponding to the restricted depth of field based upon user instructions. 7. The image processing system of claim 1, wherein the image processing pipeline application further configures the processor to generate a restricted depth of field depth map by: generating an initial depth map using the set of images captured from different viewpoints;determining pixel locations with depth estimates from the initial depth map indicating that the pixel locations are likely to have depths within the range of distances corresponding to the restricted depth of field;generating higher depth estimation precision depth estimates for at least some of the pixel locations that are likely to have depths within the range of distances corresponding to the restricted depth of field using the set of images captured from different viewpoints; andgenerating a restricted depth of field depth map using at least some of the depth estimates from the initial depth map and at least some of the higher depth estimation precision depth estimates. 8. The image processing system of claim 7, wherein the image processing pipeline application further configures the processor to generate an initial depth map by: downsampling at least some of the images in the set of images captured from different viewpoints to obtain a set of lower spatial resolution images; anddetermining a low spatial resolution depth map using the set of lower spatial resolution images. 9. The image processing system of claim 8, wherein the image processing pipeline application further configures the processor to determine a low spatial resolution depth map using the set of lower spatial resolution images by: performing a disparity search with respect to a given pixel location using the set of lower spatial resolution images;wherein the disparity search is performed by searching a first set of disparities. 10. The image processing system of claim 9, wherein the image processing pipeline application further configures the processor to generate the higher precision depth estimates by: performing a disparity search with respect to a given pixel location using the set of images captured from different viewpoints;wherein the disparity search is performed by searching a second set of disparities; andwherein a search performed using the second set of disparities provides greater depth estimation precision within the range of distances corresponding to the restricted depth of field than the precision of a depth estimate obtained within the same range of distances by a search performed using the first set of disparities. 11. The image processing system of claim 10, wherein the first set of disparities is not uniformly distributed with respect to disparity. 12. The image processing system of claim 10, wherein the first set of disparities is uniformly distributed with respect to disparity. 13. The image processing system of claim 10, wherein the second set of disparities is not uniformly distributed with respect to disparity. 14. The image processing system of claim 10, wherein the second set of disparities is uniformly distributed with respect to disparity. 15. The image processing system of claim 7, wherein the image processing pipeline application further configures the processor to: generate an initial confidence map for the initial depth map; anddetermine pixel locations with depth estimates from the initial depth map indicating that the pixel locations are likely to have depths within the range of distances corresponding to the restricted depth of field based upon the depth estimate for the pixel location in the initial depth map and the confidence of the depth estimate for the pixel location indicated by the initial confidence map. 16. The image processing system of claim 7, wherein the image processing pipeline application further configures the processor to determine pixel locations with depth estimates from the initial depth map indicating that the pixel locations are likely to have depths within the range of distances corresponding to the restricted depth of field based upon the depth estimate for the pixel location and a determination that the pixel is not contained within a textureless region. 17. The image processing system of claim 1, wherein the image processing pipeline application further configures the processor to render a restricted depth of field image from the reference viewpoint using the set of images captured from different viewpoints and the restricted depth of field depth map by: compositing pixels from the set of images captured from different viewpoints having depth estimates outside the range of distances corresponding to the restricted depth of field by applying scene dependent geometric corrections determined based upon the depth estimates of the composited pixels in the restricted depth of field depth map; andperforming super-resolution processing using pixels from the set of images captured from different viewpoints having depth estimates within the range of distances corresponding to the restricted depth of field to synthesize portions of the rendered image at a spatial resolution that is greater than the spatial resolution of the individual images in the set of images captured from different viewpoints. 18. The image processing system of claim 17, wherein the image processing pipeline application further configures the processor to perform super-resolution processing by: performing fusion of pixels from the set of images captured from different viewpoints having depth estimates within the range of distances corresponding to the restricted depth of field to obtain a set of fused pixels by applying scene dependent geometric corrections determined based upon the depth estimates of the fused pixels in the restricted depth of field depth map; andinterpolating the set of fused pixels to achieve increases in spatial resolution. 19. The image processing system of claim 17, wherein: the super-resolution processing synthesizes portion of the rendered image at a spatial resolution that is greater than the spatial resolution of the individual images in the set of images captured from different viewpoints by a super-resolution factor; anddepth estimation precision for pixels with depth estimates within the range of distances corresponding to the restricted depth of field is at least a precision with respect to disparity corresponding to the spatial resolution of the pixels of at least one of the images in the set of images captured from different viewpoints divided by the super-resolution factor. 20. The image processing system of claim 17, wherein the image processing pipeline application further configures the processor to: generate a restricted depth of field depth map by: generating an initial depth map using the set of images captured from different viewpoints by: downsampling at least some of the images in the set of images captured from different viewpoints to obtain a set of lower spatial resolution images; anddetermining a low spatial resolution depth map using the set of lower spatial resolution images;determining pixel locations with depth estimates from the initial depth map indicating that the pixel locations are likely to have depths within the range of distances corresponding to the restricted depth of field;generating higher depth estimation precision depth estimates for at least some of the pixel locations that are likely to have depths within the range of distances corresponding to the restricted depth of field using the set of images captured from different viewpoints; andgenerating a restricted depth of field depth map using at least some of the depth estimates from the initial depth map and at least some of the higher depth estimation precision depth estimates; andcomposite pixels from the set of images captured from different viewpoints and pixels from the set of lower spatial resolution images by applying scene dependent geometric corrections to the pixels from the set of lower spatial resolution images determined based upon the depth estimates in the initial depth map. 21. The image processing system of claim 1, wherein: the set of images captured from different viewpoints comprises a plurality of subsets of images captured from different viewpoints in a plurality of different color channels;the image processing pipeline application further configures the processor to render a restricted depth of field image from the reference viewpoint using the set of images captured from different viewpoints and the restricted depth of field depth map by: rendering images from each of the plurality of different color channels using the restricted depth of field depth map; andcompositing the rendered image from each of the plurality of different color channels to form a full color reduced depth of field image. 22. The image processing system of claim 1, wherein the reference viewpoint is a virtual viewpoint. 23. An array camera, comprising: an array of cameras configured to capture image data forming a set of images captured from different viewpoints;a processor;memory containing an image processing pipeline application;wherein the image processing pipeline application configures the processor to: capture a set of images captured from different viewpoints using the array of cameras;store the set of images captured from different viewpoints in memory;determine a desired range of distances corresponding to a restricted depth of field; andgenerate a restricted depth of field depth map from the reference viewpoint using the set of images captured from different viewpoints, where depth estimation precision is higher for pixels with depth estimates within the range of distances corresponding to the restricted depth of field and lower for pixels with depth estimates outside of the range of distances corresponding to the restricted depth of field;determine a range of distances corresponding to a restricted depth of field;wherein the image processing pipeline application further configures the processor to generate a restricted depth of field depth map by: performing a disparity search with respect to a given pixel location using the set of images captured from different viewpoints;wherein the disparity search is performed using a greater density of depth samples within the range of distances corresponding to the restricted depth of field and a lower density of depth samples for distances outside the range of distances corresponding to the restricted depth of field. 24. The array camera of claim 23, further comprising: a display;wherein the image processing pipeline application further configures the processor to generate a preview image from the set of images captured from different viewpoints and display the preview image via the display. 25. The array camera of claim 24, wherein: the display provides a touch user interface; andthe image processing pipeline application further configures the processor to determine a desired restricted depth of field based upon a touch gesture received via the touch user interface during the display of the preview image. 26. The array camera of claim 23, wherein: at least one of the cameras in the array of cameras includes an autofocus module configured to determine an autofocus distance; andthe image processing pipeline application configures the processor to determine a restricted depth of field based upon the autofocus distance. 27. The array camera of claim 23, wherein the array of cameras includes a π filter group comprising and a 3×3 array of cameras comprising: a reference camera at the center of the 3×3 array of cameras;two red color cameras located on opposite sides of the 3×3 array of cameras;two blue color cameras located on opposite sides of the 3×3 array of cameras; andfour green color cameras surrounding the reference camera. 28. An array camera, comprising: an array of cameras configured to capture image data forming a set of images captured from different viewpoints, where the array of cameras includes a π filter group comprising and a 3×3 array of cameras comprising: a reference camera at the center of the 3×3 array of cameras;two red color cameras located on opposite sides of the 3×3 array of cameras;two blue color cameras located on opposite sides of the 3×3 array of cameras; andfour green color cameras surrounding the reference camera;a processor; memory containing an image processing pipeline application;wherein the image processing pipeline application configures the processor to:capture a set of video sequences from different viewpoints using the array of cameras, where each video sequence comprises a sequence of frames of video and corresponding frames from the set of video sequences captured from different viewpoints form sets of frames of video captured from different viewpoints;store the set of video sequences captured from different viewpoints in memory;for a set of frames of video captured from different viewpoints: determine a desired focal plane distance and a range of distances corresponding to a restricted depth of field for a frame of video rendered from a reference viewpoint corresponding to the viewpoint of the reference camera;generate a restricted depth of field depth map from the reference viewpoint using the set of frames of video captured from different viewpoints by: generating an initial depth map using the set of frames of video captured from different viewpoints by: downsampling at least some of the frames of video in the set of frames of video captured from different viewpoints to obtain a set of lower spatial resolution images; and determining a low spatial resolution depth map using the set of lower spatial resolution images; determining pixel locations with depth estimates from the initial depth map indicating that the pixel locations are likely to have depths within the range of distances corresponding to the restricted depth of field; generating higher depth estimation precision depth estimates for at least some of the pixel locations that are likely to have depths within the range of distances corresponding to the restricted depth of field using the set of images captured from different viewpoints; and generating a restricted depth of field depth map using at least some of the depth estimates from the initial depth map and at least some of the higher depth estimation precision depth estimates; and rendering a restricted depth of field frame of video from the reference viewpoint using the set of frames of video captured from different viewpoints and the restricted depth of field depth map by: compositing pixels from the set of frames of video captured from different viewpoints having depth estimates outside the range of distances corresponding to the restricted depth of field by applying scene dependent geometric corrections determined based upon the depth estimates of the composited pixels in the restricted depth of field depth map; and performing super-resolution processing using pixels from the set of frames of video captured from different viewpoints having depth estimates within the range of distances corresponding to the restricted depth of field to synthesize portions of the rendered restricted depth of field frame of video at a spatial resolution that is greater than the spatial resolution of the individual frames of video in the set of frames of video captured from different viewpoints.
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이 특허에 인용된 특허 (146)
Hines, Stephen P, 3-D motion-parallax portable display software application.
Wilburn, Bennett; Joshi, Neel; Levoy, Marc C.; Horowitz, Mark, Apparatus and method for capturing a scene using staggered triggering of dense camera arrays.
Iwase Toshihiro (Nara JPX) Kanekura Hiroshi (Yamatokouriyama JPX), Apparatus for and method of converting a sampling frequency according to a data driven type processing.
Boisvert, David Michael; McMahon, Andrew Kenneth John, CCD output processing stage that amplifies signals from colored pixels based on the conversion efficiency of the colored pixels.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Capturing and processing of images including occlusions captured by arrays of luma and chroma cameras.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Capturing and processing of images including occlusions captured by camera arrays.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Capturing and processing of images including occlusions focused on an image sensor by a lens stack array.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H., Capturing and processing of images using monolithic camera array with heterogeneous imagers.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Capturing and processing of images using monolithic camera array with heterogeneous imagers.
Yamashita,Syugo; Murata,Haruhiko; Iinuma,Toshiya; Nakashima,Mitsuo; Mori,Takayuki, Device and method for converting two-dimensional video to three-dimensional video.
Pertsel, Shimon; Meitav, Ohad; Pozniansky, Eli; Galil, Erez, Digital camera with selectively increased dynamic range by control of parameters during image acquisition.
Ward, Gregory John; Seetzen, Helge; Heidrich, Wolfgang, Electronic camera having multiple sensors for capturing high dynamic range images and related methods.
Abell Gurdon R. (West Woodstock CT) Cook Francis J. (Topsfield MA) Howes Peter D. (Sudbury MA), Method and apparatus for arraying image sensor modules.
Sawhney,Harpreet Singh; Tao,Hai; Kumar,Rakesh; Hanna,Keith, Method and apparatus for synthesizing new video and/or still imagery from a collection of real video and/or still imagery.
Han, Hee-chul; Choi, Yang-lim; Cho, Seung-ki, Method of generating image data by an image device including a plurality of lenses and apparatus for generating image data.
Alexander David H. (Santa Monica CA) Hershman George H. (Carlsbad CA) Jack Michael D. (Carlsbad CA) Koda N. John (Vista CA) Lloyd Randahl B. (San Marcos CA), Monolithic imager for near-IR.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, System and methods for measuring depth using an array camera employing a bayer filter.
Lelescu, Dan; Molina, Gabriel; Venkataraman, Kartik, Systems and methods for dynamic refocusing of high resolution images generated using images captured by a plurality of imagers.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, Systems and methods for estimating depth and visibility from a reference viewpoint for pixels in a set of images captured from different viewpoints.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, Systems and methods for generating depth maps and corresponding confidence maps indicating depth estimation reliability.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Systems and methods for generating depth maps using a set of images containing a baseline image.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H., Systems and methods for generating depth maps using light focused on an image sensor by a lens element array.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, Systems and methods for measuring depth in the presence of occlusions using a subset of images.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, Systems and methods for measuring depth using an array of independently controllable cameras.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H., Systems and methods for parallax measurement using camera arrays incorporating 3 x 3 camera configurations.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, Systems and methods for performing depth estimation using image data from multiple spectral channels.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H., Systems and methods for performing post capture refocus using images captured by camera arrays.
Lelescu, Dan; Molina, Gabriel; Venkataraman, Kartik, Systems and methods for synthesizing high resolution images using images captured by an array of independently controllable imagers.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Systems and methods for synthesizing higher resolution images using a set of images containing a baseline image.
Ludwig, Lester F., Vignetted optoelectronic array for use in synthetic image formation via signal processing, lensless cameras, and integrated camera-displays.
Rieger Albert,DEX ; Barclay David ; Chapman Steven ; Kellner Heinz-Andreas,DEX ; Reibl Michael,DEX ; Rydelek James G. ; Schweizer Andreas,DEX, Watertight body for accommodating a photographic camera.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Capturing and processing of images including occlusions focused on an image sensor by a lens stack array.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H.; Duparre, Jacques; Hu, Shane Ching-Feng, Capturing and processing of images using camera array incorperating Bayer cameras having different fields of view.
Srikanth, Manohar; Ramamoorthi, Ravi; Venkataraman, Kartik; Chatterjee, Priyam, System and methods for depth regularization and semiautomatic interactive matting using RGB-D images.
Nayar, Shree; Venkataraman, Kartik; Pain, Bedabrata; Lelescu, Dan, Systems and methods for controlling aliasing in images captured by an array camera for use in super resolution processing using pixel apertures.
Lelescu, Dan; Venkataraman, Kartik, Systems and methods for controlling aliasing in images captured by an array camera for use in super-resolution processing.
Duparre, Jacques; McMahon, Andrew Kenneth John; Lelescu, Dan; Venkataraman, Kartik; Molina, Gabriel, Systems and methods for detecting defective camera arrays and optic arrays.
Ciurea, Florian; Venkataraman, Kartik; Molina, Gabriel; Lelescu, Dan, Systems and methods for estimating depth and visibility from a reference viewpoint for pixels in a set of images captured from different viewpoints.
Venkataraman, Kartik; Lelescu, Dan; Molina, Gabriel, Systems and methods for generating compressed light field representation data using captured light fields, array geometry, and parallax information.
Venkataraman, Kartik; Jabbi, Amandeep S.; Mullis, Robert H., Systems and methods for generating depth maps using a camera arrays incorporating monochrome and color cameras.
Duparre, Jacques; McMahon, Andrew Kenneth John; Lelescu, Dan, Systems and methods for manufacturing camera modules using active alignment of lens stack arrays and sensors.
Lelescu, Dan; Duong, Thang, Systems and methods for synthesizing high resolution images using image deconvolution based on motion and depth information.
Venkataraman, Kartik; Nisenzon, Semyon; Chatterjee, Priyam; Molina, Gabriel, Systems and methods for synthesizing images from image data captured by an array camera using restricted depth of field depth maps in which depth estimation precision varies.
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