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
收藏
页码：892 / +
页数：2
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