A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets

Abstract

In this paper, we present a framework for the solution of inverse scattering
problems that integrates traditional imaging methods and deep learning. The goal is
to image piece-wise homogeneous targets and it is pursued in three steps. First, rawdata
are processed via Orthogonality Sampling Method to obtain a qualitative image
of the targets. Then, such an image is fed into a U-Net. In order to take advantage of
the implicitly sparse nature of the information to be retrieved, the network is trained
to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an
augmented shape is turned into a map of the unknown permittivity by means of a simple
post-processing. The framework is computationally effective, since all processing steps
are performed in real-time. To provide an example of the achievable performance,
Fresnel experimental data have been used as a validation.