An Improved Deep Learning Scheme for Solving 2-D and 3-D Inverse Scattering Problems

Abstract

Reconstructing the exact electromagnetic property of unknown targets from the measured scattered field is challenging due to the intrinsic nonlinearity and ill-posedness. In this article, a new scheme, named the modified contrast scheme (MCS), is proposed to tackle nonlinear inverse scattering problems (ISPs). A local-wave amplifier coefficient is used to form the modified contrast, which is able to alleviate the global nonlinearity in original ISPs without decreasing the accuracy of the problem formulation. Moreover, the modified contrast is more suitable to be the input of the deep learning scheme, due to the unity bound of the modified contrast. The numerical results show that MCS with the modified contrast input performs well in both 2-D and 3-D testing examples in real time after offline training process, even in high-relative-permittivity cases. Compared with the dominant current scheme, a significant improvement is achieved in reconstructing high-contrast scatterers.