Neural Inverse Scattering with Score-based Regularization
This work addresses a fundamental challenge in imaging applications like microscopy and remote sensing, but it appears incremental as it builds on existing neural field methods with a new regularization technique.
The paper tackles the inverse scattering problem in imaging by jointly estimating images and scattering fields using a neural field approach with score-based regularization, achieving better imaging quality than state-of-the-art methods on simulated high-contrast objects.
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.