Parameter estimation in Bayesian Blind Deconvolution with super Gaussian image priors

Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos

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

5 Scopus citations

Abstract

Super Gaussian (SG) distributions have proven to be very powerful prior models to induce sparsity in Bayesian Blind Deconvolution (BD) problems. Their conjugate based representations make them specially attractive when Variational Bayes (VB) inference is used since their variational parameters can be calculated in closed form with the sole knowledge of the energy function of the prior model. In this work we show how the introduction in the SG distribution of a global strength (not necessary scale) parameter can be used to improve the quality of the obtained restorations as well as to introduce additional information on the global weight of the prior. A model to estimate the new unknown parameter within the Bayesian framework is provided. Experimental results, on both synthetic and real images, demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1632-1636
Number of pages5
ISBN (Electronic)9780992862619
StatePublished - Nov 10 2014
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal
Duration: Sep 1 2014Sep 5 2014

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other22nd European Signal Processing Conference, EUSIPCO 2014
CountryPortugal
CityLisbon
Period9/1/149/5/14

Keywords

  • Bayesian methods
  • Super Gaussian
  • blind deconvolution
  • image processing
  • image restoration

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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