A Fast Linearised Augmented Lagrangian Method for a Mean Curvature Based Model

被引：7
|
作者：
Zhang, Jun [1 ,2 ]
Deng, Chengzhi [1 ]
Shi, Yuying [3 ]
Wang, Shengqian [1 ]
Zhu, Yonggui [4 ]
机构：
[1] Nanchang Inst Technol, Jiangxi Prov Key Lab Water Informat Cooperat Sens, Nanchang 330099, Jiangxi, Peoples R China
[2] Nanchang Inst Technol, Coll Sci, Nanchang 330099, Jiangxi, Peoples R China
[3] North China Elect Power Univ, Dept Math & Phys, Beijing 102206, Peoples R China
[4] Commun Univ China, Sch Sci, Beijing 100024, Peoples R China
来源：
EAST ASIAN JOURNAL ON APPLIED MATHEMATICS | 2018年 / 8卷 / 03期
关键词：
Image denoising; mean curvature; linearised augmented Lagrangian method; closed form solution; shrinkage operator; NONLINEAR MULTIGRID METHOD; SPLIT BREGMAN ITERATION; EULERS ELASTICA; FAST ALGORITHM; DUAL METHODS; NOISE; IMAGES; ROF;
D O I：
10.4208/eajam.010817.160218
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A simple and efficient algorithm for solving a mean curvature based model is proposed. It uses linearisation technique and allows to find closed form solutions of all the subproblems involved. The experimental results show that the method is more efficient in terms of CPU time than the augmented Lagrangian methods considered earlier. Numerical examples demonstrate the convergence of the method.
引用
收藏
页码：463 / 476
页数：14
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