Interferometric image reconstruction as a nonlinear Bayesian estimation problem
نویسندگان
چکیده
This paper formulates interferometric image reconstriiction 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 iinterferometry, magnetic resonance imaging, and diffraction tomography.
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