Interferometric image reconstruction as a nonlinear Bayesian estimation problem
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.