期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 3, 页码 600-611出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2521428
关键词
Greedy algorithm; image denoising; joint sparse representation (JSR); nonlocal similarity; weighted sparse coding
类别
资金
- University of Macau
- National Natural Science Foundation of China [61572540]
- Macau Science and Technology Development Fund [019/2015/A]
Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and out-liers. Our model is desirable to exploit the common information shared by these data samples while reducing the influence of outliers. To solve the WJSR model, we further introduce a greedy algorithm called weighted simultaneous orthogonal matching pursuit to efficiently approximate the global optimal solution. Then, we apply the WJSR for mixed noise removal by jointly coding the grouped nonlocal similar image patches. The denoising performance is further improved by incorporating it with the global prior and the sparse errors into a unified framework. Experimental results show that our denoising method is superior to several state-of-the-art mixed noise removal methods.
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