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In this work, we focus on the inverse medium scattering problem (IMSP), whichaims to recover unknown scatterers based on measured scattered data. Motivatedby the efficient direct sampling method (DSM) introduced in [23], we propose anovel direct sampling-based deep learning approach (DSM-DL)for reconstructinginhomogeneous scatterers. In particular, we use the U-Net neural network tolearn the relation between the index functions and the true contrasts. Ourproposed DSM-DL is computationally efficient, robust to noise, easy toimplement, and able to naturally incorporate multiple measured data to achievehigh-quality reconstructions. Some representative tests are carried out withvarying numbers of incident waves and different noise levels to evaluate theperformance of the proposed method. The results demonstrate the promisingbenefits of combining deep learning techniques with the DSM for IMSP.

A Direct Sampling-Based Deep Learning Approach for Inverse Medium Scattering Problems