Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems

TL;DR

This paper introduces an implicit continuous representation approach to electromagnetic inverse scattering problems, improving solution quality by addressing discretization issues and outperforming existing methods on benchmarks.

Contribution

The novel implicit representation method effectively solves EISP by overcoming discretization challenges and enabling better inverse estimation.

Findings

Outperforms existing methods on benchmark datasets

Addresses low-resolution issues from discretization

Provides a continuous implicit model for scatterer permittivity

Abstract

Electromagnetic Inverse Scattering Problems (EISP) have gained wide applications in computational imaging. By solving EISP, the internal relative permittivity of the scatterer can be non-invasively determined based on the scattered electromagnetic fields. Despite previous efforts to address EISP, achieving better solutions to this problem has remained elusive, due to the challenges posed by inversion and discretization. This paper tackles those challenges in EISP via an implicit approach. By representing the scatterer's relative permittivity as a continuous implicit representation, our method is able to address the low-resolution problems arising from discretization. Further, optimizing this implicit representation within a forward framework allows us to conveniently circumvent the challenges posed by inverse estimation. Our approach outperforms existing methods on standard benchmark datasets. Project page: https://luo-ziyuan.github.io/Imaging-Interiors