A Discrete/Continuous Minimization Method in Interferometric Image Processing
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Filtering of interferometric SAR phase images as a fuzzy matching-pursuit blind estimation
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
Discontinuity preserving phase unwrapping using graph cuts
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Phase unwrapping via graph cuts
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
An application of the principle of minimal frustration to phase unwrapping
Digital Signal Processing
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The problem of phase unwrapping of two-dimensional (2-D) phase signals has gained a considerable interest. It deals with the problem of estimating (reconstructing) an absolute phase from the observation of its noisy principal (wrapped) values. This is an ill-posed problem since many possible solutions correspond to a given observation. Many phase unwrapping algorithms have been proposed relying on different constraints for the phase signal sampling process or the nature (e.g., smoothness, regularity) of the phase signal. We look at these algorithms from the Bayesian point of view (estimation theory) and analyze the role of the prior assumptions, studying their equivalencies to the regularization constraints already used. This study leads to the development of the two new phase unwrapping algorithms which are able to work in quite difficult conditions of aliasing and noise. The theoretical study of the analyzed schemes is illustrated by some experiments on synthetic phase signals