Score-Based Diffusion Models as Principled Priors for Inverse Imaging

Berthy T. Feng1,2    Jamie Smith2    Michael Rubinstein2    Huiwen Chang2   
Katherine L. Bouman1    William T. Freeman2   

1California Institute of Technology   2Google Research


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.

ICCV paper about exact score-based priors [pdf]
preprint about efficient score-based priors [pdf]
code [GitHub]


Berthy T. Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L. Bouman, and William T. Freeman. "Score-Based Diffusion Models as Principled Priors for Inverse Imaging." ICCV, 2023.

Berthy T. Feng and Katherine L. Bouman. "Efficient Bayesian Computational Imaging with a Surrogate Score-Based Prior." arXiv (preprint), 2023.