Local Bayesian image restoration using variational methods and gamma-normal distributions

Javier Mateos, Thomas Bishop, Rafael Molina, Aggelos K. Katsaggelos

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

1 Citation (Scopus)

Abstract

In this paper we present a new Bayesian methodology for the restoration of blurred and noisy images. Bayesian methods rely on image priors that encapsulate prior image knowledge and avoid the ill-posedness of image restoration problems. We use a spatially varying image prior utilizing a Gamma-Normal hyperprior distribution on the local precision parameters. This kind of hyperprior distribution, which to our knowledge has not been used before in image restoration, allows for the incorporation of information on local as well as global image variability, models correlation of the local precision parameters and is a conjugate hyperprior to the image model used in the paper. The proposed restoration technique is compared with other image restoration approaches, demonstrating its improved performance. ©2009 IEEE.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
Pages129-132
Number of pages4
DOIs
Publication statusPublished - 2009
Event16th IEEE International Conference on Image Processing 2009 - Cairo, Egypt
Duration: 7 Nov 200912 Nov 2009

Conference

Conference16th IEEE International Conference on Image Processing 2009
Abbreviated titleICIP 2009
CountryEgypt
CityCairo
Period7/11/0912/11/09

Keywords

  • Bayes procedures
  • Gamma-normal distributions
  • Image restoration
  • Variational methods

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  • Cite this

    Mateos, J., Bishop, T., Molina, R., & Katsaggelos, A. K. (2009). Local Bayesian image restoration using variational methods and gamma-normal distributions. In 2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings (pp. 129-132) https://doi.org/10.1109/ICIP.2009.5414169