Enhanced Deep Learning Approach Based on the Conditional Generative Adversarial Network for Electromagnetic Inverse Scattering Problems

This communication proposes a novel deep-learning (DL) framework for the electromagnetic inverse scattering (EMIS) problems. Solving EMIS problems is a challenging topic due to various difficulties, such as intrinsic nonlinearity, high computation cost, high contrast, and so on. To overcome these challenges, a novel DL-inspired approach is presented in the context of conditional deep convolutional generative adversarial network (CDCGAN), termed CDCGAN-EMIS. The proposed CDCGAN is based on a generator with an EM forward solver and the corresponding discriminator, both constructed by deep convolutional neural networks (DConvNets). During the offline training step, the generator learns a distribution between the measured scattered field data and the corresponding contrasts (permittivities) of dielectric scatterers, while the discriminator determines whether the presented samples are real or fake. Therefore, such CDCGAN-EMIS can generate contrasts of scatterers from measured scattered field data, by learning the distribution between the known contrasts of scatterers and their corresponding field and generating solutions. Based on the proposed CDCGAN-EMIS, EMIS problems can be accurately solved even for extremely high-contrast scatterers. Numerical examples indicate the accuracy and feasibility of our method. The proposed CDCGAN-EMIS opens a novel path for the DL-inspired real-time quantitative microwave imaging method for high-contrast scatterers.