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.
