The Moreau envelope approach for the L1/TV image denoising model

Feishe Chen; Lixin Shen; Yuesheng Xu; Xueying Zeng

doi:10.3934/ipi.2014.8.53

Article Contents

2014, Volume 8, Issue 1: 53-77. Doi: 10.3934/ipi.2014.8.53

This issue Previous Article The "exterior approach" to solve the inverse obstacle problem for the Stokes system Next Article Solving inverse source problems by the Orthogonal Solution and Kernel Correction Algorithm (OSKCA) with applications in fluorescence tomography

The Moreau envelope approach for the L1/TV image denoising model

1.
Department of Mathematics, Syracuse University, Syracuse, NY 13244
2.
Department of Mathematics, Syracuse University, Syracuse, NY 13244-1150
3.
School of Mathematical Sciences, Ocean University of China, Qingdao 266100

Received: September 30, 2012

Revised: July 31, 2013

Published: January 2014

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Abstract

This paper presents the Moreau envelope viewpoint for the L1/TV image denoising model. The main algorithmic difficulty for the numerical treatment of the L1/TV model lies in the non-differentiability of both the fidelity and regularization terms of the model. To overcome this difficulty, we propose five modified L1/TV models by replacing one or two non-differentiable functions in the L1/TV model with their corresponding Moreau envelopes. We prove that several existing approaches for the L1/TV model essentially solve some of the modified models, but not the original L1/TV model. Algorithms for the L1/TV model and its five variants are proposed under a unified framework based on fixed-point equations (via the proximity operator) which characterize the solutions of the models. Depending upon whether we smooth the regularization term or not, two different types of proximity algorithms are presented. The convergence rates of both types of the algorithms are improved significantly by exploring either the strategy of the Gauss-Seidel iteration, or the FISTA, or both. We compare the performance of various modified L1/TV models for the problem of impulse noise removal, and make recommendations based on our numerical experiments for using these models in applications.

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Mathematics Subject Classification: Primary: 94A08; Secondary: 49N45, 68U10.

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