Bayesian blind deconvolution with general sparse image priors

S. Derin Babacan, Rafael Molina, Minh N. Do, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Number of pages15
EditionPART 6
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 6
Volume7577 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th European Conference on Computer Vision, ECCV 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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