Group Sparsity Residual with Non-Local Samples for Image Denoising

TL;DR

This paper introduces GSR-NLS, a novel image denoising method that leverages non-local samples as references for group sparsity residual estimation, achieving superior denoising performance with competitive speed.

Contribution

It proposes using non-local samples as references in the GSR framework, improving accuracy and efficiency over previous methods like GMM or BM3D.

Findings

Outperforms many state-of-the-art denoising methods.

Delivers competitive speed in image processing.

Enhances residual estimation accuracy using non-local self-similarity.

Abstract

Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.