Combining wavelet transform and compressive sensing for subsurface imaging of non-sparse targets

Microwave imaging represent an emerging tool in the framework of noninvasive diagnostics, due to the potential advantages of providing quantitative characterizations of the inspected domains. In general, handling such a problem is not an easy task, but under some simplifying hypotheses it is possible to reduce the ill-posedness of the inverse problem and even to employ some linearization strategies. In this paper, a robust method for the quantitative imaging of weak buried objects in the framework of Ground Penetrating Radar applications by exploiting the theory of Compressed Sensing (CS) and Wavelet decomposition (WT) in a canonical two-dimensional configuration is proposed.