Image restoration combining Tikhonov with different order nonconvex nonsmooth regularizations

被引：2
|
作者：
Liu, Xiaoguang [1 ]
Gao, Xingbao [1 ]
Xue, Qiufang [1 ,2 ]
机构：
[1] Shaanxi Normal Univ, Coll Math & Informat Sci, Xian 710062, Peoples R China
[2] Xian Univ Technol, Dept Appl Math, Xian 710054, Peoples R China
来源：
2013 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2013年
关键词：
Tikhonov; different order; nonconvex nonsmooth; piecewise-smooth; neat boundary; GNC method; image restoration; LINE PROCESSES; RECONSTRUCTION; ALGORITHM;
D O I：
10.1109/CIS.2013.59
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
For piecewise-smooth images with neat boundaries, Tikhonov regularization usually makes images overly smooth, and first order nonconvex nonsmooth regularizations could cause staircase artifacts. Moreover, the image boundaries may be blurred by only utilizing the second difference to reduce staircase artifacts. To overcome above drawbacks, in this paper, piecewise-smooth images with neat boundaries are restored by the GNC method based on combining Tikhonov with different order nonconvex nonsmooth regularizations. This method could both restore the smooth parts and protect the neat boundaries more efficiently. The numerical results are used to show the restored performance of the proposed method.
引用
收藏
页码：250 / 254
页数：5
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