Parameter-free restoration algorithms for two classes of binary MRF images degraded by flip-flap noises

The problem of restoring binary (black and white) images degraded by color-dependent flip-flap noises is considered. The real image is modeled by a Markov Random Field (MRF). The Iterated Conditional Modes (ICM) algorithm is adopted. It is shown that under certain conditions the ICM algorithm is insensitive to the MRF image model and noise parameters. Using this property, we propose a parameter-free restoration algorithm which does not require the estimations of the image model and noise parameters and thus can be implemented fully in parallel. The effectiveness of the proposed algorithm is shown through applying the algorithm to degraded hand-drawn and synthetic images.