Adaptive Non-convex Non-smooth Regularization for Image Restoration Based on Spatial Information

被引:0
|
作者
Zuo, Zhiyong [1 ,2 ]
Liu, Li [1 ]
Hu, Jing [1 ]
Lan, Xia [2 ,3 ]
机构
[1] Huazhong Univ Sci & Technol, Natl Key Lab Sci & Technol Multispectral Informat, Wuhan 430074, Peoples R China
[2] 10th Inst China Elect Technol Grp Corp, Chengdu 610036, Peoples R China
[3] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China
来源
2013 6TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), VOLS 1-3 | 2013年
基金
中国国家自然科学基金;
关键词
Image restoration; Image deblurring; Non-convex non-smooth; total variation; DECONVOLUTION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Non-convex non-smooth (NCNS) regularization has advantages over convex regularization for restoring images, but it may lead to challenging computation. In this paper, an adaptive non-convex non-smooth (ANCNS) regularization is proposed for image restoration by using the spatial information indicator. Moreover, an efficient numerical algorithm for solving the resulting minimization problem is provided by applying the variable splitting and the penalty techniques. Finally, its advantages are shown in deblurring edges and restoring fines of image simultaneously in experiments.
引用
收藏
页码:341 / 345
页数:5
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