Title :
The application of the Gibbs-Bogoliubov-Feynman inequality in mean field calculations for Markov random fields
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA
fDate :
7/1/1996 12:00:00 AM
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;
Journal_Title :
Image Processing, IEEE Transactions on
