Group Sparse Coding for Image Denoising

TL;DR

This paper introduces a progressive image denoising algorithm based on group sparse coding that effectively leverages nonlocal self-similarity, resulting in superior denoising performance compared to existing methods.

Contribution

It proposes a novel progressive denoising approach that adapts group sparse representation for improved image denoising results.

Findings

Outperforms several state-of-the-art denoising methods

Effectively exploits nonlocal self-similarity in images

Provides stable and satisfactory denoising results

Abstract

Group sparse representation has shown promising results in image debulrring and image inpainting in GSR [3] , the main reason that lead to the success is by exploiting Sparsity and Nonlocal self-similarity (NSS) between patches on natural images, and solve a regularized optimization problem. However, directly adapting GSR[3] in image denoising yield very unstable and non-satisfactory results, to overcome these issues, this paper proposes a progressive image denoising algorithm that successfully adapt GSR [3] model and experiments shows the superior performance than some of the state-of-the-art methods.