Sparse Bayesian blind image deconvolution with parameter estimation

Bruno Amizic*, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


In this article, we propose a novel blind image deconvolution method developed within the Bayesian framework. We concentrate on the restoration of blurred photographs taken by commercial cameras to show its effectiveness. The proposed method is based on a non-convex l p quasi norm with 0<p<1 that is used for the image, and a total variation (TV) based prior that is utilized for the blur. Bayesian inference is carried out by utilizing bounds for both the image and blur priors using a majorization-minimization principle. Maximum a posteriori estimates of the unknown image, blur and model parameters are calculated. Experimental results (i.e., restorations of more than 30 blurred photographs) are presented to demonstrate the advantage of the proposed method compared to existing ones.

Original languageEnglish (US)
Article number20
JournalEurasip Journal on Image and Video Processing
StatePublished - 2012

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering

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