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We present a novel iterative algorithm for detection of binary Markov random fields (MRFs) corrupted by 2-D intersymbol interference (ISI) and additive white Gaussian noise. We assume a first-order binary MRF as a simple model for correlated images. We assume a 2-D digital storage channel, where the MRF is interleaved before being written and then read by a 2-D transducer; such channels occur in recently proposed optical disk storage systems. The detection algorithm is a concatenation of two soft-input/soft-output (SISO) detectors: an iterative row-column soft-decision feedback (IRCSDF) ISI detector, and a MRF detector. The MRF detector is a SISO version of the stochastic relaxation algorithm by Geman and Geman. On the 2times2 averaging-mask ISI channel, at a bit error rate of 10-4, the concatenated algorithm achieves SNR savings of between 0.2 and 2.0 dB over the IRCSDF detector alone; the savings increase as the MRFs become more correlated, or as the SNR decreases. The algorithm is also fairly robust to mismatches between the assumed and actual MRF parameters.
Date of Publication: July 2008