Data-driven restoration of degraded binary MRF images using EM method

A data-driven restoration algorithm is developed for binary images degraded nonadditively with flip noises. The true image is assumed to be a realization of a Markov random field (MRF), and the nonadditive flip noises are assumed to be statistically independent and asymmetric. The parameter estimations of the noise and the true image are carried out iteratively in the context of an expectation and maximization (EM) procedure by approximating Baum's auxiliary function of the EM procedure. To approximate Baum's auxiliary function, the estimate of the true image at each EM iteration is used. Simulation examples show the effectiveness of the data-driven restoration algorithm and the parameter estimation procedures.<
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