Inverse scattering beyond Born approximation via rotation-equivariance-aware neural network and low-rank structure

TL;DR

This paper introduces a hybrid approach combining rotation-equivariance-aware neural networks and low-rank structures to improve inverse scattering solutions beyond the Born approximation, with stability and noise filtering.

Contribution

It presents a novel hybrid method (ULR) integrating neural networks and low-rank structures, extending to limited aperture data, and compares it with other neural network approaches.

Findings

The ULR method effectively filters high-frequency noise.

The methods are stable in the Born region.

The proposed approaches outperform black-box neural networks in experiments.

Abstract

This work proposes a hybrid method (ULR) which integrates a rotation-equivariance-aware neural network and a low-rank structure to solve the two dimensional inverse medium scattering problem. The neural network is to model the data corrector which maps the full data to the Born data, and the low-rank structure is to design an inverse Born solver that finds a regularized solution from the perturbed Born data. The proposed rotation-equivariance-aware neural network naturally incorporates the reciprocity relation and the rotation-equivariance in inverse scattering, while the low-rank structure effectively filters high-frequency noise in the output of the neural network and leads to a regularized method supported by theoretical stability in the Born region. For a comparative study, we replace the low-rank inverse Born solver by another rotation-equvariance-aware neural network to propose a two-step neural network (UU). Furthermore, we extend the proposed methods (ULR and UU) to tackle the more challenging case with only limited aperture data. A variety of numerical experiments are conducted to compare the proposed ULR, UU, and a black-box neural network.