  Some types of medical and topographic imaging device produce images in which
the pixel values are "phase-wrapped", i.e. measured modulus a known scalar.
Phase unwrapping can be viewed as the problem of inferring the number of shifts
between each and every pair of neighboring pixels, subject to an a priori
preference for smooth surfaces, and subject to a zero curl constraint, which
requires that the shifts must sum to 0 around every loop. We formulate phase
unwrapping as a mean field inference problem in a Markov network, where the
prior favors the zero curl constraint. We compare our mean field technique with
the least squares method on a synthetic 100x100 image, and give results on a
512x512 synthetic aperture radar image from Sandia National Laboratories.<Long
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