It is important in computational imaging to understand the uncertainty of images reconstructed from imperfect measurements. We propose turning score-based diffusion models into principled priors (``score-based priors'') for analyzing a posterior of images given measurements. Previously, probabilistic priors were limited to handcrafted regularizers and simple distributions. In this work, we empirically validate the theoretically-proven probability function of a score-based diffusion model. We show how to sample from resulting posteriors by using this probability function for variational inference. Our results, including experiments on denoising, deblurring, and interferometric imaging, suggest that score-based priors enable principled inference with a sophisticated, data-driven image prior.

翻译：在计算成像中，理解由非完美测量重建图像的不确定性至关重要。我们提出将基于得分的扩散模型转化为规范性先验（“得分先验”），用于分析给定测量值的图像后验。此前，概率先验局限于手工设计的正则化项和简单分布。在本工作中，我们通过实验验证了基于得分的扩散模型在理论上已被证明的概率函数。我们展示了如何利用该概率函数进行变分推断，从而从生成的后验中采样。我们的结果（包括去噪、去模糊和干涉成像实验）表明，得分先验能够借助复杂的、数据驱动的图像先验实现规范性推断。