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

Haiyong Liao*, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)

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.

Original languageEnglish
Article number5565466
Pages (from-to)670-680
Number of pages11
JournalIEEE Transactions on Image Processing
Volume20
Issue number3
DOIs
Publication statusPublished - Mar 2011

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

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

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