An Early Fusion Deep Learning Framework for Solving Electromagnetic Inverse Scattering Problems

This article presents a novel early fusion framework (EFF) of deep learning (DL) for solving electromagnetic (EM) inverse-scattering problems (ISPs) in real time with high accuracy. The EFF integrates the scattered field encoder (SFE) and the backpropagation encoder (BPE). On the one hand, the nonlinear relationships constrained by the multiple scattering phenomenon, are introduced in the SFE, which guarantees better external scattering manifestations. On the other hand, the pre-reconstructed backpropagation (BP) approximation distributions provide prior information in ISPs via the BPE, which is in accordance with the internal scattering manifestations. The novel EFF concatenates the features extracted from the scattered field and the BP approximation, achieving more scattering manifestations in the solving process. Furthermore, the multiple scattering information and BP distributions regularize each other's encoding process effectively, which achieves faster convergence and more stable inversion results. It is expected that the EFF exploits a general framework for solving ISPs, which can be adopted to different scenarios by importing different kinds of prior information according to the fusion approach. The superior veracity, stability, and generalization ability of the EFF have been demonstrated by the synthetic and the experimental results.