Inverse scattering beyond Born approximation via rotation-equivariance-aware neural network and low-rank structure
For researchers in inverse scattering, this work provides a novel approach that integrates physical symmetries and low-rank regularization to improve solution stability and accuracy.
The paper proposes a hybrid method (ULR) combining a rotation-equivariance-aware neural network and a low-rank structure to solve the 2D inverse medium scattering problem beyond the Born approximation. The method achieves stable and regularized solutions, outperforming a black-box neural network in numerical experiments.
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.