Unrolled Convolutional Neural Network for Full-Wave Inverse Scattering

Abstract

An unrolled deep learning scheme for solving full-wave nonlinear inverse scattering problems (ISPs) is proposed. Inspired by the so- called unrolled method, an iterative neural network structure combining the contrast source inversion (CSI) method and residual network (ResNet) is designed. By embedding the CSI iterations into the deep learning model, the domain knowledge is well incorporated into the learning process. Thorough numerical tests are carried out to evaluate the performance, stability, robustness, and reliability of the proposed approach. Comparisons with the widely used U-net structure and CSI exhibit the advantage of the proposed approach.