Provable Diffusion-Based Posterior Sampling for Linear Inverse Problems via DDIM
Researchers introduce PDDIM, a new algorithm for solving linear inverse problems using diffusion priors. The method modifies standard DDIM updates with coordinate-wise adjustments based on signal-to-noise ratio, and is proven to converge to the Bayesian posterior. Empirical results demonstrate that PDDIM performs favorably compared to existing diffusion-based posterior samplers across various image restoration tasks.
Why it matters: This work offers a practical and theoretically grounded approach to posterior sampling in inverse problems, combining empirical effectiveness with provable guarantees.
Full story at: arXiv Statistical ML ↗