Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study
A new research paper proposes a unified framework for integrating various data-driven priors into Bayesian inverse problems. The study demonstrates how diverse priors, including regularization-by-denoising, normalizing flow-based priors, and score-based generative models, can be unified through their score functions. This approach allows for effective integration into a proposed sampling algorithm, with experimental validation in image inpainting and super-resolution tasks. AI
IMPACT This research offers a unified framework for integrating various data-driven priors, potentially improving performance in tasks like image restoration and inverse problem solving.