Total variation blind deconvolution using a variational approach to parameter, image, and blur estimation

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

*Corresponding author for this work

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

Abstract

In this paper we propose novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Within a hierarchical Bayesian formulation, the reconstructed image, the blur and the unknown hyperparameters for the image prior, the blur prior and the image degradation noise are simultaneously estimated. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Different values can be drawn from these distributions as estimates to the latent variables and the uncertainty of these estimates can be measured. Experimental results are provided to demonstrate the performance of the algorithms.

Original languageEnglish (US)
Title of host publication15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings
Pages2164-2168
Number of pages5
StatePublished - Dec 1 2007
Event15th European Signal Processing Conference, EUSIPCO 2007 - Poznan, Poland
Duration: Sep 3 2007Sep 7 2007

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other15th European Signal Processing Conference, EUSIPCO 2007
CountryPoland
CityPoznan
Period9/3/079/7/07

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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    Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2007). Total variation blind deconvolution using a variational approach to parameter, image, and blur estimation. In 15th European Signal Processing Conference, EUSIPCO 2007 - Proceedings (pp. 2164-2168). (European Signal Processing Conference).