RLGC: residual low rank group sparsity constraint for image denoising

Xiao-Xue Deng; Haoze Sun; Guoxia Xu; Yu-Feng Yu; Hao Wang

doi:10.1117/12.2605808

28 October 2021 RLGC: residual low rank group sparsity constraint for image denoising

Xiao-Xue Deng, Haoze Sun, Guoxia Xu, Yu-Feng Yu, Hao Wang

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841E (2021) https://doi.org/10.1117/12.2605808
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Image denoising is an important topic in the field of image processing. With the application of nonlocal similarity in sparse representation, the work of image denoising began to be performed on similar patch groups. The sparse representations of patches in a group will be learned together. In this paper, we propose a novel image denoising model by combining group sparsity residual with low-rankness. Firstly, motivated by the relationship between low rank and sparsity, a low rank constraint is imposed on the sparse coefficient matrix of each similar patch group to enhance the sparsity. Secondly, since 𝛾-norm can most closely match the true rank of a matrix, it is applied for rank approximation in our model. Finally, in view of the fact that numerous iterations are required in the group sparse representation (GSR) model, we develop an efficient algorithm based on the Majorize-Minimization (MM) optimization. It greatly reduces the computational complexity and the number of iterations. Experimental results show that our model makes great improvements in image denoising and outperforms many state-of-the-art methods.

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Xiao-Xue Deng, Haoze Sun, Guoxia Xu, Yu-Feng Yu, and Hao Wang "RLGC: residual low rank group sparsity constraint for image denoising", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841E (28 October 2021); https://doi.org/10.1117/12.2605808

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KEYWORDS

Image denoising

Denoising

Optimization (mathematics)

Image restoration

Quantitative analysis

Visualization

Image processing

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