Parameter estimation in total variation blind deconvolution

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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In this paper we present a methodology for parameter estimation in total variation (TV) blind deconvolution. By formulating the problem in a Bayesian framework, the unknown image, blur and the model parameters are simultaneously estimated. The resulting algorithms provide approximations to the posterior distributions of the unknowns by utilizing variational distribution approximations. We show that some of the current approaches towards TV-based blind deconvolution are special cases of our formulation. Experimental results are provided to demonstrate the performance of the algorithms. copyright by EURASIP.

Original languageEnglish (US)
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2008
Event16th European Signal Processing Conference, EUSIPCO 2008 - Lausanne, Switzerland
Duration: Aug 25 2008Aug 29 2008

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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