Sparse Bayesian blind image deconvolution with parameter estimation

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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

In this paper we propose a novel blind image deconvolution method developed within the Bayesian framework. A variant of the non-convex l p-norm prior with 0 < p < 1 is used as the image prior and a total variation (TV) based prior is utilized as the blur prior. The proposed method is derived by utilizing bounds for both the image and blur priors using the majorization-minimization principle. Maximum a posteriori Bayesian inference is performed and as a result, the unknown image, blur and model parameters are simultaneously estimated. We also show that as a special case, the developed method provides very competitive non-blind image restoration results when the blurring function is assumed to be known. Experimental results are presented to demonstrate the advantage of the proposed method compared to existing ones.

Original languageEnglish (US)
Pages (from-to)626-630
Number of pages5
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2010
Event18th European Signal Processing Conference, EUSIPCO 2010 - Aalborg, Denmark
Duration: Aug 23 2010Aug 27 2010

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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