Restoration of noisy images modeled by Markov random fields with Gibbs distribution

The authors develop techniques for restoration of noisy images using Markov/Gibbs random fields. In the schemes to be presented, the local characteristics of the noise-free image are described by pairwise-interaction Markov random fields, while the noise, assumed to be mainly additive, is modeled as a zero-mean Gaussian process. The estimation of the clean image is based on the MAP criterion. Optimal estimates are derived with proper choice of performance criteria. Studies undertaken with a variety of images have confirmed the feasibility of the proposed techniques under conditions of high noise