Bayesian restoration of image sequences using 3-D Markov random fields

The authors describe a method for restoring sequences of noisy images obtained by acquiring different views of the same scene. The method uses a 3-D Markov random field and a least-square-error matching to establish the temporal-spatial neighborhood of a pixel in an image under restoration. The problem of image sequence restoration is posed as the problem of maximizing the conditional probabilities. This task is accomplished by a modified version of the iterated conditional modes method where Gibbs distribution is used to model the prior probability