An iteratively reweighted norm algorithm for minimization of total variation functionals

被引：91
作者：
Wohlberg, Brendt [1 ]
Rodriguez, Paul [1 ]
机构：
[1] Los Alamos Natl Lab, Math Modeling & Anal T 7, Los Alamos, NM 87545 USA
来源：
IEEE SIGNAL PROCESSING LETTERS | 2007年 / 14卷 / 12期
关键词：
image restoration; inverse problem; regularization; total variation;
D O I：
10.1109/LSP.2007.906221
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard l(2) data fidelity term with an l(1) norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the l(2)-TV and l(1)-TV problems as special cases. This method offers competitive computational performance for l(2)-TV and is comparable to or faster than any other l(1)-TV algorithms of which we are aware.
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页码：948 / 951
页数：4
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