Abstract
The problem of phase unwrapping of two-dimensional phase signals has gained a considerable interest in recent years. The areas of applications include speckle imaging, magnetic resonance imaging and Synthetic Aperture Radar (SAR) interferometry. Phase unwrapping deals with the problem of estimating an absolute phase f from the observation of its noisy principal (wrapped) values g. This is an ill-posed problem since many possible solutions correspond to a given observation. We give an overview of several phase unwrapping algorithms and analyze them in the Bayesian framework, discussing the quality of models used to encapsulate the a priori knowledge.
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