Boosting normalized sparsity regularization for blind image deconvolution-Reference-Cited by-同舟云学术

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Boosting normalized sparsity regularization for blind image deconvolution

Published:2016-11-17 Issue:4 Volume:11 Page:681-688
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Yang Chang-Xing,Shao Wen-Ze,Huang Li-Li

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

National Natural Science Foundation of China (CN)

Natural Science Foundation of Guangxi Province

Jiangsu Key Laboratory of Image and Video Understanding for Social Safety

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://link.springer.com/content/pdf/10.1007/s11760-016-1010-6.pdf

Reference26 articles.

1. Chambolle, A., Lions, P.-L.: Image recovery via total variation minimization and related problems. Numer. Math. 76, 167–188 (1997)

2. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.: Removing camera shake from a single photograph. ACM Trans. Graph. 25(3), 787–794 (2006)

3. Babacan, S.D., Molina, R., Katsaggelos, A.K.: Variational Bayesian blind deconvolution using a total variation prior. IEEE Trans. Image Process. 18(1), 12–26 (2009)

4. Shao, W.-Z., Deng, H.-S., Ge, Q., Li, H.-B., Wei, Z.-H.: Regularized motion blur-kernel estimation with adaptive sparse image prior learning. Pattern Recognit. 51, 402–424 (2016)

5. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. IEEE CVPR (2011). doi: 10.1109/CVPR.2011.5995308

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