The application of the Gibbs-Bogoliubov-Feynman inequality in mean field calculations for Markov random fields

Author

Zhang, Jun

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA

Volume

5

Issue

7

fYear

1996

fDate

7/1/1996 12:00:00 AM

Firstpage

1208

Lastpage

1214

Abstract

The Gibbs-Bogoliubov-Feynman (GBF) inequality of statistical mechanics is adopted, with an information-theoretic interpretation, as a general optimization framework for deriving and examining various mean field approximations for Markov random fields (MRF´s). The efficacy of this approach is demonstrated through the compound Gauss-Markov (CGM) model, comparisons between different mean field approximations, and experimental results in image restoration

Keywords

Gaussian processes; Markov processes; image restoration; optimisation; random processes; Gibbs-Bogoliubov-Feynman inequality; Markov random fields; compound Gauss-Markov model; image restoration; information-theoretic interpretation; mean field approximations; mean field calculations; optimization framework; statistical mechanics; Computer networks; Computer vision; Gaussian approximation; Image generation; Image processing; Image restoration; Least squares approximation; Markov random fields; Parallel processing; Radio access networks; Timing; User-generated content; Velocity measurement;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.502411

Filename

502411