  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
