Bayesian estimation with Gauss-Markov-Potts priors in optical diffraction tomography

Hacheme Ayasso; Bernard Duchêne; Ali Mohammad-Djafari

doi:10.1117/12.872317

7 February 2011 Bayesian estimation with Gauss-Markov-Potts priors in optical diffraction tomography

Hacheme Ayasso, Bernard Duchêne, Ali Mohammad-Djafari

Proceedings Volume 7873, Computational Imaging IX; 78730U (2011) https://doi.org/10.1117/12.872317
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

In this paper, Optical Diffraction Tomography (ODT) is considered as an inverse scattering problem. The goal is to retrieve a map of the electromagnetic parameters of an unknown object from measurements of the scattered electric field that results from its interaction with a known interrogating wave. This is done in a Bayesian estimation framework. A Gauss-Markov-Potts prior appropriately translates the a priori knowledge that the object is made of a finite number of homogeneous materials distributed in compact 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 experimental laboratory controlled data, will illustrate the performances of the proposed method which is compared to the more classical Contrast Source Inversion method (CSI) developed in a deterministic framework.

Citation Download Citation

Hacheme Ayasso, Bernard Duchêne, and Ali Mohammad-Djafari "Bayesian estimation with Gauss-Markov-Potts priors in optical diffraction tomography", Proc. SPIE 7873, Computational Imaging IX, 78730U (7 February 2011); https://doi.org/10.1117/12.872317

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