Efficient Chambolle-Pock based algorithms for Convoltional sparse representation

TL;DR

This paper introduces a fast Chambolle-Pock based algorithm for convolutional sparse representation that avoids manual parameter tuning and demonstrates improved convergence and noise removal performance over existing methods.

Contribution

The paper proposes a novel Chambolle-Pock framework for convolutional sparse coding and dictionary learning, eliminating the need for penalty parameter tuning and enhancing convergence speed.

Findings

Achieves comparable results to ADMM in noise-free scenarios

Outperforms ADMM in denoising Gaussian noise images

Faster convergence without manual parameter selection

Abstract

Recently convolutional sparse representation (CSR), as a sparse representation technique, has attracted increasing attention in the field of image processing, due to its good characteristic of translate-invariance. The content of CSR usually consists of convolutional sparse coding (CSC) and convolutional dictionary learning (CDL), and many studies focus on how to solve the corresponding optimization problems. At present, the most efficient optimization scheme for CSC is based on the alternating direction method of multipliers (ADMM). However, the ADMM-based approach involves a penalty parameter that needs to be carefully selected, and improper parameter selection may result in either no convergence or very slow convergence. In this paper, a novel fast and efficient method using Chambolle-Pock(CP) framework is proposed, which does not require extra manual selection parameters in solving processing, and has faster convergence speed. Furthermore, we propose an anisotropic total variation penalty of the coefficient maps for CSC and apply the CP algorithm to solve it. In addition, we also apply the CP framework to solve the corresponding CDL problem. Experiments show that for noise-free image the proposed CSC algorithms can achieve rival results of the latest ADMM-based approach, while outperforms in removing noise from Gaussian noise pollution image.