Abstract
The problem of imaging arbitrary-shaped targets is addressed through a methodological generalization of the compressive sensing (CS) paradigm. The Color CS (C-CS) methodology consists of a two-level hierarchical scheme where, at the first level and using a basis dictionary , several sparsity-regularized inversions are performed in parallel, while, at the second level, the retrieved expansion coefficients are compared to select the most reliable reconstruction according to a sparsity-rewarding criterion. Within the Born-approximated formulation of the microwave imaging problem, a Bayesian solver and a filtered $\ell _{0}$ -norm criterion are employed to implement the first and the second C-CS levels, respectively. Selected numerical results, representative of an extensive validation, are presented to illustrate the features, the advantages, and the limitations of C-CS also in comparison with some competitive state-of-the-art inversion techniques.
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