Presentation on theme: "Blind motion deblurring from a single image using sparse approximation"— Presentation transcript:
1 Blind motion deblurring from a single image using sparse approximation Jian-Feng Caiy, Hui Jiz, Chaoqiang Liuy and Zuowei ShenzNational University of Singapore, SingaporeCenter for Wavelets, Approx. and Info. Proc.y and Department of MathematicszCVPR 2009報告者：黃智勇2017/4/6
2 Outline Introduction Tight framelet system and curvelet system Sparse representation under framelet and curvelet systemFormulation of our minimizationNumerical algorithm and analysisExperiments2017/4/6
3 IntroductionWe propose to use framelet system (Ron and Shen et al. ) to find the sparse approximation to the image under framelet domain.We use the curvelet system (Candes and Donoho ) to find the sparse approximation to the blur kernel under curvelet domain.2017/4/6
5 Sparse representation under framelet and curvelet system 2017/4/6
6 Formulation of our minimization We denote the image g (or the kernel p) as a vector g (or p). Let “。” denote the usual 2D convolution after column concatenation, then we haveLet u = Ag denote the framelet coefficients of the clear image g, and let v = Cp denote the curvelet coefficients of the blur kernel p.2017/4/6
7 Numerical algorithm and analysis there exist only two difficult problems (14) and (15) of the same type. For such a large-scale minimization problem with up to millions of variables, there exists a very efficient algorithm based on so-called linearized Bregman iteration technique.2017/4/6
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