Convergence analysis of the Bregman method for the variational model of image denoising

被引：55
|
作者：
Jia, Rong-Qing [1 ]
Zhao, Hanqing [1 ]
Zhao, Wei [1 ]
机构：
[1] Univ Alberta, Dept Math & Stat Sci, Edmonton, AB T6G 2G1, Canada
来源：
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS | 2009年 / 27卷 / 03期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Bregman method; Optimization; Total variation; Image denoising; Wavelet packets; ITERATIVE REGULARIZATION;
D O I：
10.1016/j.acha.2009.05.002
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The total variation model of Rudin, Osher, and Fatemi for image denoising is considered to be one of the best denoising models. Recently, by using the Bregman method, Goldstein and Osher obtained a very efficient algorithm for the solution of the ROF model. In this paper, we give a rigorous proof for the convergence of the Bregman method. We also indicate that a combination of the Bregman method with wavelet packet decomposition often enhances performance for certain texture rich images. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码：367 / 379
页数：13
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