SEMI-LOCAL TOTAL VARIATION FOR REGULARIZATION OF INVERSE PROBLEMS

被引：0
|
作者：
Condat, Laurent [1 ]
机构：
[1] Univ Grenoble Alpes, Dept Images & Signals, GIPSA Lab, Grenoble, France
来源：
2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2014年
关键词：
total variation; non-local regularization; inverse problem; convex optimization; proximal method;
D O I：
暂无
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：
We propose the discrete semi-local total variation (SLTV) as a new regularization functional for inverse problems in imaging. The SLTV favors piecewise linear images; so the main drawback of the total variation (TV), its clustering effect, is avoided. Recently proposed primal-dual methods allow to solve the corresponding optimization problems as easily and efficiently as with the classical TV.
引用
收藏
页码：1806 / 1810
页数：5
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