DocumentCode :
854887
Title :
Bayesian multichannel image restoration using compound Gauss-Markov random fields
Author :
Molina, Rafael ; Mateos, Javier ; Katsaggelos, Aggelos K. ; Vega, Miguel
Author_Institution :
Dept. de Ciencias de la Computacion e I.A., Univ. de Granada, Spain
Volume :
12
Issue :
12
fYear :
2003
Firstpage :
1642
Lastpage :
1654
Abstract :
We develop a multichannel image restoration algorithm using compound Gauss-Markov random fields (CGMRF) models. The line process in the CGMRF allows 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 and their 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.
Keywords :
Bayes methods; Gaussian processes; Markov processes; convergence of numerical methods; image colour analysis; image restoration; iterative methods; parameter estimation; simulated annealing; Bayesian multichannel image restoration; color images; compound Gauss-Markov random fields; convergence; iterative algorithms; iterative conditional methods; line process; simulated annealing methods; Bayesian methods; Convergence; Gaussian processes; Image color analysis; Image restoration; Iterative algorithms; Laplace equations; Layout; Markov random fields; Simulated annealing;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2003.818015
Filename :
1257400
Link To Document :
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