scholarly journals Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique

2010 ◽  
Vol 37 (8) ◽  
pp. 4210-4226 ◽  
Author(s):  
Jacob D. Shea ◽  
Panagiotis Kosmas ◽  
Susan C. Hagness ◽  
Barry D. Van Veen
Keyword(s):  
Inverse Scattering ◽  
Three Dimensional ◽  
Microwave Imaging ◽  
Multiple Frequency ◽  
Scattering Technique ◽  
Breast Phantoms

scholarly journals Microwave Imaging of Three-Dimensional Targets by Means of an Inexact-Newton-Based Inversion Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Claudio Estatico ◽  
Matteo Pastorino ◽  
Andrea Randazzo
Keyword(s):  
Inverse Scattering ◽  
Scattering Problem ◽  
Three Dimensional ◽  
Inverse Scattering Problem ◽  
Microwave Imaging ◽  
Inexact Newton Method ◽  
Imaging Method ◽  
Inversion Algorithm ◽  
Three Dimensional Objects ◽  
Full Vector

A microwave imaging method previously developed for tomographic inspection of dielectric targets is extended to three-dimensional objects. The approach is based on the full vector equations of the electromagnetic inverse scattering problem. The ill-posedness of the problem is faced by the application of an inexact-Newton method. Preliminary reconstruction results are reported.

scholarly journals Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach

2019 ◽  
Vol 5 (1) ◽  
pp. 19 ◽  
Author(s):  
Marco Salucci ◽  
Lorenzo Poli ◽  
Giacomo Oliveri
Keyword(s):  
Compressive Sensing ◽  
Inverse Scattering ◽  
Computational Efficiency ◽  
State Of The Art ◽  
Three Dimensional ◽  
Microwave Imaging ◽  
Source Inversion ◽  
Contrast Source Inversion ◽  
Probable Distribution ◽  
Most Probable Distribution

In this paper, the full-vectorial three-dimensional (3D) microwave imaging (MI) of sparse scatterers is dealt with. Towards this end, the inverse scattering (IS) problem is formulated within the contrast source inversion (CSI) framework and it is aimed at retrieving the sparsest and most probable distribution of the contrast source within the imaged volume. A customized multi-task Bayesian compressive sensing (MT-BCS) method is used to yield regularized solutions of the 3D-IS problem with a remarkable computational efficiency. Selected numerical results on representative benchmarks are presented and discussed to assess the effectiveness and the reliability of the proposed MT-BCS strategy in comparison with other competitive state-of-the-art approaches, as well.

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