An Improved Variational Method for Image Denoising

TL;DR

This paper introduces an improved total variation model for image denoising that effectively removes various noise types, guarantees convergence, and outperforms existing TV models in quality.

Contribution

The paper presents a novel TV model with a unique solution and a convergent algorithm, enhancing denoising performance over previous methods.

Findings

Effective removal of multiple noise types

Guaranteed convergence of the algorithm

Superior denoising quality in experiments

Abstract

The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose an improved TV model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noises and their combinations. Our improved model admits a unique solution and the associated numerical algorithm guarantees the convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing.