Deep Learning-Based Inversion Methods for Solving Inverse Scattering Problems With Phaseless Data

This is the authors' abstract. We don't add key points for this paper.

Without phase information of the measured field data, the phaseless data inverse scattering problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full data ISPs (FD-ISPs). In this article, we propose a learning-based inversion approach in the frame of the U-net convolutional neural network (CNN) to quantitatively image unknown scatterers located in homogeneous background from the amplitude-only measured total field (also denoted PD). Three training schemes with different inputs to the U-net CNN are proposed and compared, i.e., the direct inversion scheme (DIS) with phaseless total field data, retrieval dominant induced currents by the Levenberg-Marquardt (LM) method (PD-DICs), and PD with contrast source inversion (PD-CSI) scheme. We also demonstrate the setup of training data and compare the performance of the three schemes using both numerical and experimental tests. It is found that the proposed PD-CSI and PD-DICs perform better in terms of accuracy, generalization ability, and robustness compared with DIS. PD-CSI has the strongest capability to tackle with PD-ISPs, which outperforms the PD-DICs and DIS.