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Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Range estimation by optical differentiation
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Multi-camera Scene Reconstruction via Graph Cuts
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Depth Measurement by the Multi-Focus Camera
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Analyzing the visual echo: Passive 3-D imaging with a multiple aperture camera
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ACM SIGGRAPH 2006 Papers
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ACM SIGGRAPH 2006 Papers
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image and depth from a conventional camera with a coded aperture
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High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
4D frequency analysis of computational cameras for depth of field extension
ACM SIGGRAPH 2009 papers
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ACM SIGGRAPH 2010 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Spatio-angular resolution tradeoffs in integral photography
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Optimized aperture shapes for depth estimation
Pattern Recognition Letters
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We present a novel method for solving blind deconvolution, i.e., the task of recovering a sharp image given a blurry one. We focus on blurry images obtained from a coded aperture camera, where both the camera and the scene are static, and allow blur to vary across the image domain. As most methods for blind deconvolution, we solve the problem in two steps: First, we estimate the coded blur scale at each pixel; second, we deconvolve the blurry image given the estimated blur. Our approach is to use linear high-order priors for texture and second-order priors for the blur scale map, i.e., constraints involving two pixels at a time. We show that by incorporating the texture priors in a least-squares energy minimization we can transform the initial blind deconvolution task in a simpler optimization problem. One of the striking features of the simplified optimization problem is that the parameters that define the functional can be learned offline directly from natural images via singular value decomposition. We also show a geometrical interpretation of image blurring and explain our method from this viewpoint. In doing so we devise a novel technique to design optimally coded apertures. Finally, our coded blur identification results in computing convolutions, rather than deconvolutions, which are stable operations. We will demonstrate in several experiments that this additional stability allows the method to deal with large blur. We also compare our method to existing algorithms in the literature and show that we achieve state-of-the-art performance with both synthetic and real data.