Wavelet-Based Compressive Imaging of Sparse Targets

Abstract

The application of the compressive sensing (CS) paradigm to retrieve non-single-pixels contrast profiles is discussed. By exploiting a wavelet representation to model complex scatterer distributions with sparse vectors of coefficients, an efficient Bayesian CS (BCS) strategy is adopted to solve the arising inverse scattering problem. A set of representative numerical examples is presented to illustrate the advantages and the limitations of the proposed approach also with respect to comparable state-of-the-art inversion methods.