Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Understand

Diffusion models (DMs) have proven to be effective in modeling high-dimensional distributions, leading to their widespread adoption for representing complex priors in Bayesian inverse problems (BIPs).

• However, current DM-based posterior sampling methods proposed for solving common BIPs rely on heuristic approximations to the generative process.
• To exploit the generative capability of DMs and avoid the usage of such approximations, we propose an ensemble-based algorithm that performs posterior sampling without the use of heuristic approximations.
• Our algorithm is motivated by existing works that combine DM-based methods with the sequential Monte Carlo (SMC) method.