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Some types of medical and topographic imaging device produce images in whichthe 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 shiftsbetween each and every pair of neighboring pixels, subject to an a prioripreference for smooth surfaces, and subject to a zero curl constraint, whichrequires that the shifts must sum to 0 around every loop. We formulate phaseunwrapping as a mean field inference problem in a Markov network, where theprior favors the zero curl constraint. We compare our mean field technique withthe least squares method on a synthetic 100x100 image, and give results on a512x512 synthetic aperture radar image from Sandia National Laboratories.<LongText>

A Factorized Variational Technique for Phase Unwrapping in Markov Random Fields