New AI method solves thorny inverse problems with diffusion models

A new paper presents a remarkably simple and effective solution for estimating observation parameters in inverse problems where regularization uses a Bayesian framework with a diffusion model prior. The strategy defines an optimal estimator for both observation parameters and the target image, provides uncertainty quantification, and uses efficient MCMC algorithms. Numerical experiments confirm the computational efficiency and high quality of the estimates and uncertainty measures.