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Investigation of Deep Learning Backbone Influence on 2D Permittivity Reconstruction Performance

This paper investigates the influence of the deep learning (DL) backbone on the reconstruction performance of scatterers in two-dimensional inverse scattering problems (ISPs). To avoid using DL as a purely data-driven black-box solver, it would be profitable to combine DL with the understanding of the physics in ISPs. The modified contrast scheme (MCS) is selected to generate the input of the deep learning algorithm, which enables DL to reconstruct scatterers with higher contrast. As for the backbone of the deep learning algorithm, the U2-Net is selected to capture more global information in the shallow layer, which helps DL comprehensively learn the global scattering phenomenon in ISPs. Numerical results show that with the same input, the reconstruction performance of the U2-Net is better than the U-Net.