Variational Bayesian compressive blind image deconvolution

Bruno Amizic, Leonidas Spinoulas, Rafael Molina, Aggelos K Katsaggelos

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

2 Scopus citations


We propose a novel variational Bayesian framework to perform simultaneous compressive sensing (CS) image reconstruction and blind deconvolution (BID) as well as estimate all modeling parameters. Furthermore, we show that the proposed framework generalizes the alternating direction method of multipliers which is often utilized to transform a constrained optimization problem into an unconstrained one through the use of the augmented Lagrangian. The proposed framework can be easily adapted to other signal processing applications or particular image and blur priors within the proposed context. In this work, as an example, we employ the following priors to illustrate the significance of the proposed approach: (1) a non-convex lp quasi-norm based prior for the image, (2) a simultaneous auto-regressive prior for the blur, and (3) an l1 norm based prior for the transformed coefficients. Experimental results using synthetic images demonstrate the advantages of the proposed algorithm over existing approaches.

Original languageEnglish (US)
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - Jan 1 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: Sep 9 2013Sep 13 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Other2013 21st European Signal Processing Conference, EUSIPCO 2013


  • Bayesian methods
  • Inverse methods
  • blind image deconvolution
  • compressive sensing
  • parameter estimation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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