An iteratively reweighted norm algorithm for Total Variation regularization

被引：11

作者：

Rodriguez, Paul ^{[1
]}

Wohlberg, Brendt ^{[1
]}

机构：

[1] Los Alamos Natl Lab, T7 Math Modeling & Anal, POB 1663, Los Alamos, NM 87545 USA

来源：

2006 FORTIETH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, VOLS 1-5 | 2006年

关键词：

D O I：

10.1109/ACSSC.2006.354879

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. Recently, a number of authors have noted the advantages, including superior performance with certain non-Gaussian noise, of replacing the standard l(2) data fidelity term with an l(1) norm. We propose a simple but very flexible and computationally efficient method, the Iteratively Reweighted Norm algorithm, for minimizing a generalized TV functional which includes both the l(2)-TV and and l(1)-TV problems.

引用

页码：892 / +

页数：2

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