A GNC Method for Nonconvex Nonsmooth Image Restoration

被引：0
作者：
Liu, Xiao-Guang [1 ]
Xue, Qiu-fang [2 ]
机构：
[1] Southwest Univ Nationalities, Sch Comp Sci & Technol, Chengdu 610041, Sichuan, Peoples R China
[2] Xian Univ Technol, Dept Appl Math, Xian 710054, Shaanxi, Peoples R China
来源：
SMART GRAPHICS, SG 2015 | 2017年 / 9317卷
关键词：
Nonconvex nonsmooth; Augmented lagrangian; GNC method; MINIMIZATION; ALGORITHM; RECONSTRUCTION;
D O I：
10.1007/978-3-319-53838-9_16
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The augmented Lagrangian duality method have superior restoration performance for nonconvex nonsmooth images. However, an effective initial value could not be obtained for the augmented Lagrangian duality when it is used alone. To overcome this drawback, a hybrid method based on the augmented Lagrangian duality method and the graduated nonconvex method(GNC) is proposed. The better restored performance of the proposed method are illustrated by some numerical results.
引用
收藏
页码：184 / 191
页数：8
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