Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation

被引：64
|
作者：
Liao, Haiyong [1 ,2 ]
Ng, Michael K. [1 ,2 ]
机构：
[1] Hong Kong Baptist Univ, Ctr Math Imaging & Vis, Kowloon Tong, Hong Kong, Peoples R China
[2] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON IMAGE PROCESSING | 2011年 / 20卷 / 03期
关键词：
Alternating minimization; blind deconvolution; generalized cross validation (GCV); regularization parameters; total variation (TV); TOTAL VARIATION MINIMIZATION; BLUR IDENTIFICATION; IMAGE-RESTORATION; VARIATIONAL APPROACH; ALGORITHM;
D O I：
10.1109/TIP.2010.2073474
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior.
引用
收藏
页码：670 / 680
页数：11
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