用得分模型先验提升反散射成像质量
Neural Inverse Scattering with Score-based Regularization
- 用神经场联合估计图像与散射场
- 得分函数先验使成像质量优于总变差正则
- 适合高对比度物体的成像任务
反散射问题是显微成像到遥感等众多成像应用中的基础挑战。求解该问题通常需要联合估计图像和物体内部的散射场，因此需要有效的图像先验来正则化推断过程。本文提出一种基于得分函数正则化的神经场（NF）方法，将得分生成模型中的去噪得分函数融入其中。神经场结构便于联合估计，而得分函数则引入了丰富的图像结构先验。在三个高对比度模拟物体上的实验结果表明，该方法相比最先进的基于总变差正则的神经场方法，显著提升了成像质量。
原文摘要 · Abstract (English)
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.
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