Exemplar-Based Denoising: A Unified Low-Rank Recovery Framework
Published in IEEE Transactions on Circuits and Systems for Video Technology, 2019
Recommended citation: Xiaoqin Zhang, Jingjing Zheng, Di Wang and Li Zhao. "Exemplar-based Denoising: A Unified Low-rank Recovery Framework." IEEE Transactions on Circuits and Systems for Video Technology. 2019, 30(8):2538-2549. https://ieeexplore.ieee.org/abstract/document/8758211
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 l0-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.
Recommended citation: Xiaoqin Zhang, Jingjing Zheng, Di Wang and Li Zhao. Exemplar-based Denoising: A Unified Low-rank Recovery Framework. IEEE Transactions on Circuits and Systems for Video Technology. 2019, 30(8):2538-2549.