cvpr cvpr2013 cvpr2013-449 cvpr2013-449-reference knowledge-graph by maker-knowledge-mining
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Author: Li Xu, Shicheng Zheng, Jiaya Jia
Abstract: 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 L0 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 unifiedframeworkfor both uniform andnon-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.
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