http://dx.doi.org/10.1109/TIP.2010.2073474">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Blind deconvolution using generalized cross-validation approach to regularization parameter estimation

Language

English

Abstract

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. © 2011 IEEE.

Keywords

Alternating minimization, blind deconvolution, generalized cross validation (GCV), regularization parameters, total variation (TV)

Publication Date

2011

Source Publication Title

IEEE Transactions on Image Processing

Volume

20

Issue

3

Start Page

670

End Page

680

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

10577149

ISSN (electronic)

19410042

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