Variational Bayesian inference image restoration using a product of total variation-like image priors

Giannis Chantas*, Nikolaos Galatsanos, Rafael Molina, Aggelos K Katsaggelos

*Corresponding author for this work

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

Abstract

In this paper a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted Total Variations (TV). These spatial weights provide this prior with the flexibilit to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed algorithm is fully automatic in the sense that all necessary parameters are estimated from the data. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.

Original languageEnglish (US)
Title of host publication2010 2nd International Workshop on Cognitive Information Processing, CIP2010
Pages227-231
Number of pages5
DOIs
StatePublished - Nov 22 2010
Event2010 2nd International Workshop on Cognitive Information Processing, CIP2010 - Elba Island, Italy
Duration: Jun 14 2010Jun 16 2010

Other

Other2010 2nd International Workshop on Cognitive Information Processing, CIP2010
CountryItaly
CityElba Island
Period6/14/106/16/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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