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Boosting normalized sparsity regularization for blind image deconvolution

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Abstract

We focus on blind image deconvolution, which has attracted intensive attentions since Fergus et al.’s influential work in 2006. Among the current literature, the daring idea of imposing the normalized sparsity measure on blind image deblurring is a recent spotlight, which is, nevertheless, far from practical use in terms of estimating accuracy, efficiency, as well as robustness. To boost its performance, we propose a novel method via coupling the normalized sparsity measure with the total generalized variation, which, however, does not fit the blind deblurring problem. By use of operator splitting and alternating direction method of multipliers, a numerical scheme is derived, leading to a more accurate, efficient, and robust blind deblurring algorithm. Numerous blind deblurring results on both benchmark data and real-world blurred images demonstrate the competitive or even better performance compared against the state of the art.

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Notes

  1. www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip

  2. Since March, 2013, the authors of [15] have successively released two executable software (implemented in C++) for blind motion deblurring, i.e., Robust Motion Deblurring System. The first version is v3.0.1 which implements the algorithm as detailed in [15], and the second version is v3.1 which incorporates the algorithms in both [15] and [14] for more accurate blur kernel estimation. The notation [15+14] means that we use the second version, i.e., v3.1, to produce the deblurred images.

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Acknowledgements

We would like to show our gratitude to the anonymous reviewers for their serious, pertinent, and very helpful comments on the manuscript. This research was supported in part by the Natural Science Foundation (NSF) of China (61402239, 61302178, 61602257), the NSF of Jiangsu Province (BK20130868, BK20160904), and the NSF of Guangxi Province (2014GXNSFAA118360), the NSF for Jiangsu Institutions (15KJB520028, 16KJB520035), and the Jiangsu Key Lab. of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, 30920140122007).

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Correspondence to Wen-Ze Shao.

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Yang, CX., Shao, WZ. & Huang, LL. Boosting normalized sparsity regularization for blind image deconvolution. SIViP 11, 681–688 (2017). https://doi.org/10.1007/s11760-016-1010-6

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