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

翻译：电磁逆散射问题在计算成像领域已获得广泛应用。通过求解电磁逆散射问题，可以基于散射电磁场非侵入式地确定散射体的内部相对介电常数。尽管先前已有诸多尝试解决电磁逆散射问题，但由于反演和离散化带来的挑战，获得该问题的更优解始终难以实现。本文通过一种隐式方法应对电磁逆散射问题中的这些挑战。通过将散射体的相对介电常数表示为连续的隐式表示，我们的方法能够解决离散化导致的低分辨率问题。此外，在前向框架内优化这种隐式表示，使我们能够便捷地规避逆估计带来的挑战。我们的方法在标准基准数据集上超越了现有方法。项目页面：https://luo-ziyuan.github.io/Imaging-Interiors