Bayesian Regularization in Nonlinear Imaging: Reconstructions From Experimental Data in Nonlinearized Microwave Tomography

In this paper, we investigate the robustness and the effectiveness of a microwave imaging technique, based on the Bayesian estimation theory, for the reconstruction of dielectric profiles. The method has been applied and validated on real experimental data. Our statistical-based inversion algorithm takes advantage of Bayesian regularization, which permits the inversion of a strongly nonlinear model using a Markov random field as an a priori statistical model of the unknown image. Such choice leads to a robust and effective nonlinear inversion method. The exhaustive analysis performed on the experimental data shows the good performance of the method.