We present a novel iterative soft decision feedback zig-zag algorithm for detection of binary images corrupted by two dimensional intersymbol interference and additive white Gaussian noise. The algorithm exchanges soft information between maximum-a-posteriori detectors employing different zigzag scan directions. Each detector exploits soft-decision feedback from the other zig-zag detectors. Simulation results for the 2 × 2 averaging mask channel show that, at low signal-to-noise ratios, the new algorithm gains about 1 dB over an iterative row column soft decision feedback algorithm and over a separable mask algorithm, two of the best previously published schemes. When the zig-zag algorithm is concatenated with the row-column algorithm, the concatenated system performs as well as or better than four of the best previously published algorithms, at both low and high signal-to-noise ratios, for a variety of 2 × 2 and 3 × 3 convolution masks; in several cases, the system performs within less than 0.1 dB of the maximum-likelihood performance bound.

We present a novel iterative row-column soft decision feedback algorithm (IRCSDFA) for detection of binary images corrupted by 2-D intersymbol interference and additive white Gaussian noise. The algorithm exchanges weighted soft information between row and column maximuma posteriori(MAP) detectors. Each MAP detector exploits soft-decision feedback from previously processed rows or columns. The new algorithm gains about 0.3 dB over the previously best published results for the 2times2 averaging mask. For a non-separable 3times3 mask, the IRCSDFA gains 0.8 dB over a previous soft-input/soft-output iterative algorithm which decomposes the 2-D convolution into 1-D row and column operations.