Adaptive Restoration of Degraded Binary MRF Images Using EM Method

Tatsuya YAMAZAKI  Mehdi N.SHIRAZI  Hideki NODA  

IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.2   pp.259-268
Publication Date: 1993/02/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing, Computer Graphics and Pattern Recognition
image restoration,  image parameter estimation,  MRF model,  EM method,  ICM method,  

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An adaptive 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. Using the Expectation and Maximization (EM) method and approximating the Baum's auxiliary function, the degraded image is restored iteratively. The algorithm is implemented as follows. First, the unknown parameters and the true image are guessed or estimated roughly. Second, using the true image estimate, the Baum's auxiliary function is approximated and then the noise and MRF parameters are reestimated. To reestimate the MRF parameters the Maximum Pseudo-likelihood (MPL) method is used. Third, using the Iterated Conditional Modes (ICM) method, the true image is reestimated. The second and third steps are carried out iteratively until by some ad hoc criterion a critical point of EM algorithm is approximated. A number of simulation examples are presented which show the effectiveness of the algorithm and the parameter estimation procedures.