@techreport{elediasc12658,
            year = {2014},
       publisher = {University of Trento},
            type = {Technical Report},
          author = {L. Poli and G. Oliveri and F. Viani and A. Massa},
           title = {Multi-task Bayesian Compressive Sensing for microwave imaging exploiting the minimum-norm current formulation},
        abstract = {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.},
        keywords = {Compressive Sensing, Inverse Scattering, Interval Analysis},
             url = {http://www.eledia.org/students-reports/658/}
}