Image Decomposition: Theory, Numerical Schemes, and Performance Evaluation

TL;DR

This paper reviews various image decomposition models combining total variation with advanced functional spaces, and introduces a performance evaluation method to better understand their effectiveness.

Contribution

It presents a comprehensive overview of image decomposition models and proposes a new evaluation method to analyze their performance.

Findings

Models effectively separate structures, textures, and noise.

Performance evaluation method provides insights into model behavior.

Combines total variation with Besov, Contourlet, and oscillating function spaces.

Abstract

This paper describes the many image decomposition models that allow to separate structures and textures or structures, textures, and noise. These models combined a total variation approach with different adapted functional spaces such as Besov or Contourlet spaces or a special oscillating function space based on the work of Yves Meyer. We propose a method to evaluate the performance of such algorithms to enhance understanding of the behavior of these models.