Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases

TL;DR

This paper introduces NeuralBIM, a neural network-based iterative method inspired by physics principles, for efficiently solving 2D inverse scattering problems with both supervised and unsupervised training schemes, validated by numerical and experimental results.

Contribution

NeuralBIM is a novel neural network approach that emulates traditional iterative methods for inverse scattering, incorporating physics-informed learning without requiring extensive data.

Findings

NeuralBIM achieves accurate reconstructions in 2D ISPs.

Both supervised and unsupervised schemes are effective.

Numerical and experimental results confirm the method's efficacy.

Abstract

In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions regarding the residuals. Two different schemes are presented in this paper, including the supervised and unsupervised learning schemes. With the data set generated by the method of moments (MoM), supervised NeuralBIM are trained with the knowledge of total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded objective function founding on the governing equations of ISPs, which results in no requirement of total fields and contrasts for training. Numerical and experimental results further validate the efficacy of NeuralBIM.