Exemplar-Based Denoising: A Unified Low-Rank Recovery Framework

作者：Xiaoqin Zhang, Jingjing Zheng, Di Wang, Li Zhao · 发表于：IEEE Transactions on Circuits and Systems for Video Technology · 年份：2019 · DOI：10.1109/tcsvt.2019.2927603 · 被引用次数：48 · 研究领域：Image and Signal Denoising Methods、Sparse and Compressive Sensing Techniques、Advanced Image Fusion Techniques

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.