Image restoration using reduced order models
Signal Processing - Multidimensional Signal Processing, Part II
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A New Algorithm for Energy Minimization with Discontinuities
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Interferometric image reconstruction as a nonlinear Bayesian estimation problem
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Bayesian approaches to phase unwrapping: theoretical study
IEEE Transactions on Signal Processing
Model based phase unwrapping of 2-D signals
IEEE Transactions on Signal Processing
Integer parameter estimation in linear models with applications toGPS
IEEE Transactions on Signal Processing
Absolute phase image reconstruction: a stochastic nonlinear filtering approach
IEEE Transactions on Image Processing
Two-dimensional phase unwrapping using a block least-squares method
IEEE Transactions on Image Processing
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The 2D absolute phase estimation problem, in interferometric applications, is to infer absolute phase (not simply modulo-2π) from incomplete, noisy, and modulo-2π image observations. This is known to be a hard problem as the observation mechanism is nonlinear. In this paper we adopt the Bayesian approach. The observation density is 2π-periodic and accounts for the observation noise; the a priori probability of the absolute phase is modeled by a first order noncausal Gauss Markov random field (GMRF) tailored to smooth absolute phase images. We propose an iterative scheme for the computation of the maximum a posteriori probability (MAP) 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, we name the algorithm ZπM, where letter M stands for maximization. A set of experimental results, comparing the proposed algorithm with other techniques, illustrates the effectiveness of the proposed method.