Bayesian multichannel image restoration using compound Gauss-Markov random fields

Rafael Molina*, Javier Mateos, Aggelos K. Katsaggelos, Miguel Vega

*Corresponding author for this work

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

In this paper, we develop a multichannel image restoration algorithm using Compound Gauss Markov Random Fields (CGMRF) models. The line process in the CGMRF will allow the channels to share important information regarding the objects present in the scene. In order to estimate the underlying multichannel image two new iterative algorithms are presented, whose convergence is established. They can be considered as extensions of the classical simulated annealing and iterative conditional methods. Experimental results with color images demonstrate the effectiveness of the proposed approaches.

Original languageEnglish (US)
Pages (from-to)1642-1654
Number of pages13
JournalIEEE Transactions on Image Processing
Volume12
Issue number12
DOIs
StatePublished - Dec 1 2003

Keywords

  • Compound Gauss-Markov random fields
  • Iterative conditional mode
  • Multichannel image restoration
  • Simulated annealing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Bayesian multichannel image restoration using compound Gauss-Markov random fields'. Together they form a unique fingerprint.

  • Cite this