Neural Inverse Scattering with Score-based Regularization
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
Inverse scattering is a fundamental challenge in many imaging applications,
ranging from microscopy to remote sensing. Solving this problem often requires
jointly estimating two unknowns -- the image and the scattering field inside
the object -- necessitating effective image prior to regularize the inference.
In this paper, we propose a regularized neural field (NF) approach which
integrates the denoising score function used in score-based generative models.
The neural field formulation offers convenient flexibility to performing joint
estimation, while the denoising score function imposes the rich structural
prior of images. Our results on three high-contrast simulated objects show that
the proposed approach yields a better imaging quality compared to the
state-of-the-art NF approach, where regularization is based on total variation.
