Fast Full-Wave Electromagnetic Inverse Scattering Based on Scalable Cascaded Convolutional Neural Networks

Abstract

The end-to-end scalable cascaded convolutional neural networks (SC-CNNs) are proposed to solve inverse scattering problems (ISPs), and the high-resolution image can be directly obtained from the scattered field with the guiding by multiresolution labels in the cascaded blocks. To alleviate the difficulty of solving the ISPs via a full-wave way, the proposed SC-CNNs are physically decomposed into two parts, i.e., the linear transformation and the multiresolution imaging networks. The first part is composed of one CNN block and is used to mimic the linear transformation [e.g., backpropagation (BP)] from scattered field to the preliminary image, whereas the second part consists of a few cascaded CNN blocks to realize the reconstruction from the rough image to high-resolution image. With more high-frequency components incorporating into the multiresolution labels, the cascaded networks can be guided through those labels, avoiding black-box operations and enhancing the physical meaning and interpretability. The proposed SC-CNNs are verified by both the synthetic and experimental examples and it is proved that better performance can be achieved in terms of both inversion accuracy and efficiency compared to the BP-Unet and direct inversion scheme (DIS).