Bayesian regularization in non-linear imaging: Reconstructions from experimental data in microwave tomography

In this paper we investigate the robustness and the effectiveness of a microwave imaging technique, based on Bayesian estimation theory, for the reconstruction of dielectric profiles. The validation is conducted on real experimental data, the well-known “Marseille” dataset. Our statistical based inversion algorithm takes advantage of Bayesian regularization, which permits to invert a strongly non-linear model using a Markov Random Field as a-priori statistical model of the unknown image. Such choice leads to a robust and effective non-linear inversion method. An exhaustive analysis on the experimental data is also performed, in order to show the good performance of the method.