A CONDITIONAL GRADIENT METHOD FOR PRIMAL-DUAL TOTAL VARIATION-BASED IMAGE DENOISING

被引:5
|
作者
Bentbib, Abdeslem Hafid [1 ]
Bouhamidi, Abderrahman [2 ]
Kreit, Karim [1 ]
机构
[1] Univ Cadi Ayyad, Marrakech, Morocco
[2] Univ Littoral, LMPA, 50 Rue F Buisson BP699, F-62228 Calais, France
关键词
ill-posed problem; regularization; total variation; conditional gradient; image restoration; image denoising; convex programming; TIKHONOV-REGULARIZATION; CONVEX-OPTIMIZATION; ALGORITHMS;
D O I
10.1553/etna_vol48s310
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we consider the problem of image denoising by total variation regularization. We combine the conditional gradient method with the total variation regularization in the dual formulation to derive a new method for denoising images. The convergence of this method is proved. Some numerical examples are given to illustrate the effectiveness of the proposed method.
引用
收藏
页码:310 / 328
页数:19
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