Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. (English) Zbl 0573.62030

Some methods for image restoration resulting by observing analogies between images and statistical lattice based systems are discussed. Their goal is to restore degraded images by means of a Bayesian approach introducing a statistical model based on the Gibbs distribution.
The problem of maximizing the conditional distribution of the image \(X=(F,L)\) (F stands for the matrix of observable pixel intensities while L is a matrix of unobservable edge elements), given data \(G=\underline g\), i.e. \(P(X=\underline x| G=\underline g)\) is solved with the aid of stochastic relaxation.
Reviewer: W.Pedrycz


62F15 Bayesian inference
68T10 Pattern recognition, speech recognition
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