Interferometric image reconstruction as a nonlinear Bayesian estimation problem

Authors:
J. M. N. Leitao;M. A. T. Figueiredo
Affiliations:
-;-
Venue:
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Year:
1995

Citing 2
Cited 1

Nonlinear phase estimators based on the Kullback distance

ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Phase unwrapping of MR phase images using Poisson equation

IEEE Transactions on Image Processing

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

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Abstract

This paper formulates interferometric image reconstruction as a 2D absolute phase estimation problem. The original phase image is modeled as a sample of a Gauss Markov random field; the observations are the noisy in-phase (cosine) and quadrature (sine) images. The proposed solution combines features of the iterated conditional modes algorithm with nonlinear stochastic absolute phase estimation concepts. Examples of important applications are: interferometric synthetic aperture radar, optical interferometry, magnetic resonance imaging, and diffraction tomography.