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. © 2007 EURASIP.

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

AUTIERI, ROBERTA;PASCAZIO, Vito
2007

Abstract

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. © 2007 EURASIP.
9788392134022
9788392134022
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/53066
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