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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Multi-frequency Phase Unwrapping from Noisy Data: Adaptive Local Maximum Likelihood Approach
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Markov random field based phase demodulation of interferometric images
Computer Vision and Image Understanding
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
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ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Phase unwrapping via graph cuts
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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This paper presents an effective algorithm for absolute phase (not simply modulo-2-π) estimation from incomplete, noisy and modulo-2π observations in interferometric aperture radar and sonar (InSAR/InSAS). The adopted framework is also representative of other applications such as optical interferometry, magnetic resonance imaging and diffraction tomography. The Bayesian viewpoint is adopted; the observation density is 2-π-periodic and accounts for the interferometric pair decorrelation and system noise; the a priori probability of the absolute phase is modeled by a compound Gauss-Markov random field (CGMRF) tailored to piecewise smooth absolute phase images. We propose an iterative scheme for the computation of the maximum a posteriori probability (MAP) absolute phase estimate. Each iteration embodies a discrete optimization step (Z-step), implemented by network programming techniques and an iterative conditional modes (ICM) step (π-step). Accordingly, the algorithm is termed ZπM, where the letter M stands for maximization. An important contribution of the paper is the simultaneous implementation of phase unwrapping (inference of the 2π-multiples) and smoothing (denoising of the observations). This improves considerably the accuracy of the absolute phase estimates compared to methods in which the data is low-pass filtered prior to unwrapping. A set of experimental results, comparing the proposed algorithm with alternative methods, illustrates the effectiveness of our approach