Image Denoising Using Low Rank Minimization With Modified Noise Estimation

TL;DR

This paper introduces a modified low rank minimization algorithm for image denoising that incorporates geometric structure into noise estimation and utilizes singular value differences to improve edge and texture preservation.

Contribution

The paper proposes GWNNM, a novel algorithm that enhances residual noise estimation by considering geometric structure and exploits singular value differences for better denoising performance.

Findings

Significant improvement in denoising quality over existing methods.

Enhanced edge and texture preservation during denoising.

More reliable residual noise estimation in high noise scenarios.

Abstract

Recently, the application of low rank minimization to image denoising has shown remarkable denoising results which are equivalent or better than those of the existing state-of-the-art algorithms. However, due to iterative nature of low rank optimization, estimation of residual noise is an essential requirement after each iteration. Currently, this noise is estimated by using the filtered noise in the previous iteration without considering the geometric structure of the given image. This estimate may be affected in the presence of moderate and severe levels of noise. To obtain a more reliable estimate of residual noise, we propose a modified algorithm (GWNNM) which includes the contribution of the geometric structure of an image to the existing noise estimation. Furthermore, the proposed algorithm exploits the difference of large and small singular values to enhance the edges and textures during the denoising process. Consequently, the proposed modifications achieve significant improvements in the denoising results of the existing low rank optimization algorithms.