SEMISMOOTH NEWTON METHOD FOR MINIMIZATION OF THE LLT MODEL

被引：5
|
作者：
Pang, Zhi-Feng [1 ]
Yang, Yu-Fei [1 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
来源：
INVERSE PROBLEMS AND IMAGING | 2009年 / 3卷 / 04期
关键词：
Semismooth Newton method; image denoising; LLT model; Q-superlinear convergence; global convergence; REGULARIZATION; RESTORATION; ALGORITHM;
D O I：
10.3934/ipi.2009.3.677
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we discuss the nonsmooth second-order regularization, suggested by Lysaker, Lundervold and Tai, and its application in image denoising. A function space BV2 is given and the well-posedness of the LLT model is proved in this function space. By means of the Fisher-Burmeister NCP function, we reformulate the dual formula of the LLT model in discrete setting as a system of semismooth equations. Then we propose a semismooth Newton method for the LLT model to build up a Q-superlinearly convergent numerical scheme. The computational experiments are supplied to demonstrate the efficiency of the proposed method.
引用
收藏
页码：677 / 691
页数：15
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