Unnatural L0 Sparse Representation for Natural Image Deblurring

We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathemati...

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Bibliographic Details
Published in2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 1107 - 1114
Main Authors Li Xu, Shicheng Zheng, Jiaya Jia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Approximation methods
Cameras
Electric shock
Estimation
Image edge detection
Kernel
Optimization
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ISSN1063-6919
1063-6919
DOI10.1109/CVPR.2013.147

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Summary:We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L 0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.147