On semismooth Newton's methods for total variation minimization

被引：84

作者：

Ng, Michael K. ^{[1
]}

Qi, Liqun

Yang, Yu-Fei

Huang, Yu-Mei

机构：

[1] Hong Kong Baptist Univ, Ctr Math Imaging & Vis, Kowloon Tong, Hong Kong, Peoples R China

[2] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China

[3] Hong Kong Polytech Univ, Dept Appl Math, Kowloon, Hong Kong, Peoples R China

[4] Hunan Univ, Coll Math & Econometr, Changsha, Peoples R China

[5] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China

来源：

JOURNAL OF MATHEMATICAL IMAGING AND VISION | 2007年 / 27卷 / 03期

基金：

中国国家自然科学基金;

关键词：

semismooth Newton's methods; total variation; denoising; regularization;

D O I：

10.1007/s10851-007-0650-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton's methods for total variation minimization. The convergence and numerical results are also presented to show the effectiveness of the proposed algorithms.

引用

页码：265 / 276

页数：12

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