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Blind motion deblurring from a single image using sparse approximation. Jian-Feng Caiy, Hui Jiz, Chaoqiang Liuy and Zuowei Shenz National University of Singapore, Singapore 117542 Center for Wavelets, Approx. and Info. Proc.y and Department of Mathematicsz. CVPR 2009. 報告者：黃智勇. Outline.
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Blind motion deblurring from a single image using sparse approximation Jian-Feng Caiy, Hui Jiz, Chaoqiang Liuy and Zuowei Shenz National University of Singapore, Singapore 117542 Center for Wavelets, Approx. and Info. Proc.y and Department of Mathematicsz CVPR 2009 報告者：黃智勇
Outline • Introduction • Tight framelet system and curvelet system • Sparse representation under framelet andcurvelet system • Formulation of our minimization • Numerical algorithm and analysis • Experiments
Introduction We propose to use framelet system (Ron and Shen et al.[24]) to find the sparseapproximation to the image underframelet domain. Weuse the curvelet system (Candes and Donoho [8]) to find thesparse approximation to the blur kernel under curvelet domain.
Formulation of our minimization Wedenote the image g (or the kernel p) as a vector g (or p). Let “。” denote theusual 2D convolution after column concatenation, thenwehave Let u = Ag denote the framelet coefficients of the clearimage g, and let v = Cp denote the curvelet coefficientsof the blur kernel p.
Numerical algorithm and analysis there exist only two difficult problems (14) and (15) ofthe same type. For such a large-scale minimization problemwith up to millions of variables, there exists a very efficientalgorithm based on so-called linearized Bregman iterationtechnique.
