Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 30, Issue 8, Pages 2538-2549Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2019.2927603
Keywords
Image denoisng; low-rank recovery
Categories
Funding
- National Key Research and Development Program of China [2018YFB1004904]
- National Natural Science Foundation of China [61772374]
- Natural Science Foundation of Zhejiang Province [LY17F030004, LR17F030001, LQ19F020005]
- Project of Science and Technology Plans of Wenzhou City [C20170008, G20160002, ZG2017016]
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Exemplar based image denoising algorithms have shown great potential for image restoration with a multitude of existing models. In this paper, we interpret nonlocal similar patch-based denoising as a problem of low-rank recovery. This offers a physically plausible model and unifies several existing techniques in a single low-rank recovery framework. The framework can handle complex noise models, such as zero-mean Gaussian noise, impulse noise, and any other noise that can be approximated by mixing these two kinds of noise. Moreover, we introduce a new nonconvex surrogate for the l(0)-norm and find the optimal solution of the optimization problems when the new norm is applied to low-rank recovery. The experimental results with different kinds of noise confirm the effectiveness of the proposed low-rank recovery framework and the new norm.
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