relation: http://www.eledia.org/students-reports/658/
title: Multi-task Bayesian Compressive Sensing for microwave imaging exploiting the minimum-norm current formulation
creator: Poli, L.
creator: Oliveri, G.
creator: Viani, F.
creator: Massa, A.
subject: M CS Compressive Sensing
description: In this report, an innovative three-step contrast-source probabilistic technique is proposed for the reconstruction of image 2D-sparse dielectric profiles. Within the formulation of the inverse scattering problem, such an approach combines (i) a SVD-based step to retrieve the minimum-norm currents, (ii) a probabilistic reformulation of the inverse scattering problem in terms of the real and imaginary parts of the sparse contrast currents (iii) a multi-task BCS strategy for properly correlating the unknown variables (real and imaginary parts of contrast source coefficients). An enhanced version of the multi-task implementation that takes into account the correlations real and imaginary parts of contrast source coefficients related to different views is also investigated through a wide set of numerical experiments.
publisher: University of Trento
date: 2014
type: Monograph
type: NonPeerReviewed
format: text
language: en
identifier: http://www.eledia.org/students-reports/658/1/Multi-task%20Bayesian%20Compressive%20Sensing%20for%20microwave%20imaging%20exploiting%20the%20minimum-norm%20current%20formulation.pdf
identifier:   Poli, L. and Oliveri, G. and Viani, F. and Massa, A.  (2014) Multi-task Bayesian Compressive Sensing for microwave imaging exploiting the minimum-norm current formulation.  Technical Report. University of Trento.