Variational bayesian image restoration with a product of spatially weighted total variation image priors

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

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 flexibility 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 restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. 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)
Article number5272318
Pages (from-to)351-362
Number of pages12
JournalIEEE Transactions on Image Processing
Volume19
Issue number2
DOIs
StatePublished - Feb 2010

Keywords

  • No Keywords.

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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