Unnatural L0 Sparse Representation for Natural Image Deblurring

Li Xu, Shicheng Zheng, Jiaya Jia; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1107-1114

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 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.

Related Material


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[bibtex]
@InProceedings{Xu_2013_CVPR,
author = {Xu, Li and Zheng, Shicheng and Jia, Jiaya},
title = {Unnatural L0 Sparse Representation for Natural Image Deblurring},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}