Variational Bayesian blind deconvolution using a total variation prior

S. Derin Babacan*, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

160 Scopus citations

Abstract

In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.

Original languageEnglish (US)
Pages (from-to)12-26
Number of pages15
JournalIEEE Transactions on Image Processing
Volume18
Issue number1
DOIs
StatePublished - 2009

Keywords

  • Bayesian methods
  • Blind deconvolution
  • Parameter estimation
  • Total variation (TV)
  • Variational methods

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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