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In the Bayesian statistical context of smoothing noisy, or corrupted, images, Greig, Porteous and Seheult showed how the maximum a posteriori estimate of a binary image can be obtained ''exactly'' by maximising the flow through an associated image network, involving the introduction of a ''source'' and ''sink''. The problem was therefore converted into a NP hard problem which could be solved using known efficient algorithms.
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