Bayesian multichannel image restoration using compound Gauss-Markov random fields

Rafael Molina; Javier Mateos; Aggelos K. Katsaggelos; Miguel Vega

doi:10.1109/TIP.2003.818015

Bayesian multichannel image restoration using compound Gauss-Markov random fields

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

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

58 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 language	English (US)
Pages (from-to)	1642-1654
Number of pages	13
Journal	IEEE Transactions on Image Processing
Volume	12
Issue number	12
DOIs	https://doi.org/10.1109/TIP.2003.818015
State	Published - Dec 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

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10.1109/TIP.2003.818015

Cite this

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title = "Bayesian multichannel image restoration using compound Gauss-Markov random fields",

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.",

keywords = "Compound Gauss-Markov random fields, Iterative conditional mode, Multichannel image restoration, Simulated annealing",

author = "Rafael Molina and Javier Mateos and Katsaggelos, {Aggelos K.} and Miguel Vega",

note = "Funding Information: Manuscript received November 26, 2001; revised May 8, 2003. This work was supported by the “Comisi{\'o}n Nacional de Ciencia y Tecnolog{\'i}a” under Contract TIC2000-1275. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mark S. Drew.",

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doi = "10.1109/TIP.2003.818015",

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T1 - Bayesian multichannel image restoration using compound Gauss-Markov random fields

AU - Molina, Rafael

AU - Mateos, Javier

AU - Katsaggelos, Aggelos K.

AU - Vega, Miguel

N1 - Funding Information: Manuscript received November 26, 2001; revised May 8, 2003. This work was supported by the “Comisión Nacional de Ciencia y Tecnología” under Contract TIC2000-1275. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mark S. Drew.

PY - 2003/12

Y1 - 2003/12

N2 - 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.

AB - 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.

KW - Compound Gauss-Markov random fields

KW - Iterative conditional mode

KW - Multichannel image restoration

KW - Simulated annealing

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Bayesian multichannel image restoration using compound Gauss-Markov random fields

Abstract

Keywords

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