Deep Learning Electromagnetic Inversion Solver Based on Two-Step Framework for High-Contrast and Heterogeneous Scatterers

This paper proposes a novel electromagnetic (EM) inversion solver based on a two-step deep learning (DL) framework. The framework consists of the deep convolutional asymmetric encoder-decoder structure (DCAEDS) followed by the deep residual convolutional neural network (DRCNN). In the first step, DCAEDS utilizes EM scattered field data from a single-frequency one-time measurement to coarsely retrieve the initial contrasts (permittivities) of target scatterers. In the second step, DRCNN employs a mixed input scheme, comprising the initially-reconstructed permittivities from the first step and the original EM scattered field data, to significantly improve the retrieved contrasts (permittivities) and refine the reconstruction of targets. Consequently, the proposed EM inversion solver achieves excellent accuracy and efficiency, even for high-contrast targets. The proposed solver is flexible as it is required only for a single-frequency one-time measurement on the EM scattered field. Moreover, the proposed two-step DL-based solver overcomes the limitations of conventional methods, such as high computational costs and ill-posedness. Numerical benchmarks based on various dielectric objects demonstrate the feasibility of the proposed EM inversion solver, highlighting its potential as a candidate for real-time quantitative EM inversion for high-contrast targets.