Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

被引：1
作者：
Ruan, Yaduan [1 ]
Fang, Houzhang [2 ]
Chen, Qimei [1 ]
机构：
[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷
基金：
中国国家自然科学基金;
关键词：
BLIND DECONVOLUTION; RESTORATION;
D O I：
10.1155/2014/606170
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.
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收藏
页数：8
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