Bayesian inversion for optical diffraction tomography

In this paper, optical diffraction tomography is considered as a non-linear inverse scattering problem and tackled within the Bayesian estimation framework. The object under test is a man-made object known to be composed of compact regions made of a finite number of different homogeneous materials. This a priori knowledge is appropriately translated by a Gauss-Markov-Potts prior. Hence, a Gauss-Markov random field is used to model the contrast distribution whereas a hidden Potts-Markov field accounts for the compactness of the regions. First, we express the a posteriori distributions of all the unknowns and then a Gibbs sampling algorithm is used to generate samples and estimate the posterior mean of the unknowns. Some preliminary results, obtained by applying the inversion algorithm to laboratory controlled data, are presented.