Generalizable Neural Electromagnetic Inverse Scattering
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Solving Electromagnetic Inverse Scattering Problems (EISP) is fundamental in
applications such as medical imaging, where the goal is to reconstruct the
relative permittivity from scattered electromagnetic field. This inverse
process is inherently ill-posed and highly nonlinear, making it particularly
challenging. A recent machine learning-based approach, Img-Interiors, shows
promising results by leveraging continuous implicit functions. However, it
requires case-specific optimization, lacks generalization to unseen data, and
fails under sparse transmitter setups (e.g., with only one transmitter). To
address these limitations, we revisit EISP from a physics-informed perspective,
reformulating it as a two stage inverse transmission-scattering process. This
formulation reveals the induced current as a generalizable intermediate
representation, effectively decoupling the nonlinear scattering process from
the ill-posed inverse problem. Built on this insight, we propose the first
generalizable physics-driven framework for EISP, comprising a current estimator
and a permittivity solver, working in an end-to-end manner. The current
estimator explicitly learns the induced current as a physical bridge between
the incident and scattered field, while the permittivity solver computes the
relative permittivity directly from the estimated induced current. This design
enables data-driven training and generalizable feed-forward prediction of
relative permittivity on unseen data while maintaining strong robustness to
transmitter sparsity. Extensive experiments show that our method outperforms
state-of-the-art approaches in reconstruction accuracy, generalization, and
robustness. This work offers a fundamentally new perspective on electromagnetic
inverse scattering and represents a major step toward cost-effective practical
solutions for electromagnetic imaging.
