A Factorized Variational Technique for Phase Unwrapping in Markov Random Fields
Abstract
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 Text>
Cite
@article{arxiv.1301.2252,
title = {A Factorized Variational Technique for Phase Unwrapping in Markov Random Fields},
author = {Kannan Achan and Brendan J. Frey and Ralf Koetter},
journal= {arXiv preprint arXiv:1301.2252},
year = {2013}
}
Comments
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)