Imaging Interiors：一种电磁逆散射问题的隐式求解方法
Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problems
AI总结：
针对电磁逆散射问题中反演与离散化的挑战，提出基于连续隐式表征的求解方法，在正向框架内优化以规避逆估计问题、解决离散化低分辨率问题，性能优于现有方法。
摘要：
电磁逆散射问题（Electromagnetic Inverse Scattering Problems, EISP）在计算成像领域已获得广泛应用。通过求解EISP，可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。尽管已有诸多研究尝试解决EISP，但由于反演和离散化带来的挑战，获得更优的求解结果仍十分困难。本文通过一种隐式方法应对EISP中的上述挑战。通过将散射体的相对介电常数表示为连续隐式表征，本文方法能够解决离散化导致的低分辨率问题。此外，在正向框架内优化该隐式表征，可便捷地规避逆估计带来的挑战。本文方法在标准基准数据集上的表现优于现有方法。项目主页：https://luo-ziyuan.github.io/Imaging-Interiors
英文摘要：
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
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