1. 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
Center for Wavelets, Approx. and Info. Proc.y and Department of Mathematicsz
CVPR 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 system
Formulation of our minimization
Numerical algorithm and analysis
Experiments
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3. Introduction
We propose to use framelet system (Ron and Shen et al. [24]) to find the sparse approximation to the image under framelet domain.
We use the curvelet system (Candes and Donoho [8]) to find the sparse approximation to the blur kernel under curvelet domain.
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4. Tight famelet system
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5. Sparse representation under framelet and curvelet system
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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 have
Let u = Ag denote the framelet coefficients of the clear image g, and let v = Cp denote the curvelet coefficients of the blur kernel p.
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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.
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8. Numerical algorithm and analysis
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9. Experiments
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10. Experiments
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11. Experiments
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