Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Selection of Camera Parameters for Recovery of Depth from Defocused Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Extracting depth and matte using a color-filtered aperture
ACM SIGGRAPH Asia 2008 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analyzing depth from coded aperture sets
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
International Journal of Computer Vision
Statistics of real-world hyperspectral images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We propose modifying the aperture of a conventional color camera so that the effective aperture size for one color channel is smaller than that for the other two. This produces an image where different color channels have different depths-of-field, and from this we can computationally recover scene depth, reconstruct an all-focus image and achieve synthetic re-focusing, all from a single shot. These capabilities are enabled by a spatio-spectral image model that encodes the statistical relationship between gradient profiles across color channels. This approach substantially improves depth accuracy over alternative single-shot coded-aperture designs, and since it avoids introducing additional spatial distortions and is light efficient, it allows high-quality deblurring and lower exposure times. We demonstrate these benefits with comparisons on synthetic data, as well as results on images captured with a prototype lens.