Noisy image decomposition: a new structure, texture and noise model based on local adaptivity

TL;DR

This paper introduces a novel image decomposition model that separates images into structure, texture, and noise components using local adaptivity, improving handling of noisy images compared to existing methods.

Contribution

The paper proposes a new three-part decomposition model based on local regularization, extending previous two-part models to better manage noise in images.

Findings

Effective separation of structure, texture, and noise in noisy images

Comparison shows improved performance over existing models

A combined model leverages advantages of previous approaches

Abstract

These last few years, image decomposition algorithms have been proposed to split an image into two parts: the structures and the textures. These algorithms are not adapted to the case of noisy images because the textures are corrupted by noise. In this paper, we propose a new model which decomposes an image into three parts (structures, textures and noise) based on a local regularization scheme. We compare our results with the recent work of Aujol and Chambolle. We finish by giving another model which combines the advantages of the two previous ones.