Empirical Bayesian Imaging With Large-Scale Push-Forward Generative Priors

We propose a new methodology for leveraging deep generative priors for Bayesian inference in imaging inverse problems. Modern Bayesian imaging often relies on score-based diffusion generative priors, which deliver remarkable point estimates but significantly underestimate uncertainty. Push-forward models such as variational auto-encoders and generative adversarial networks provide a robust alternative, leading to Bayesian models that are provably well-posed and which produce accurate uncertainty quantification results for small problems. However, push-forward models scale poorly to large problems because of issues related to bias, mode collapse and multimodality. We propose to address this difficulty by embedding a conditional deep generative prior within an empirical Bayesian framework. We consider generative priors with a super-resolution architecture, and perform inference by using a Bayesian computation strategy that simultaneously computes the maximum marginal likelihood estimate (MMLE) of the low-resolution image of interest, and draws Monte Carlo samples from the posterior distribution of the high-resolution image, conditionally to the observed data and the MMLE. The methodology is demonstrated with an image deblurring experiment and comparisons with the state-of-the-art.