Benchmarking Uncertainty Quantification of Plug-and-Play Diffusion Priors for Inverse Problems Solving
This work tackles the critical need for uncertainty-aware evaluation in scientific inverse problems, though it is incremental as it builds on existing diffusion priors.
The paper addresses the lack of uncertainty quantification in evaluations of plug-and-play diffusion priors for inverse problems, proposing a systematic benchmark and categorization that reveals consistent uncertainty behaviors in simulations and real-world applications.
Plug-and-play diffusion priors (PnPDP) have become a powerful paradigm for solving inverse problems in scientific and engineering domains. Yet, current evaluations of reconstruction quality emphasize point-estimate accuracy metrics on a single sample, which do not reflect the stochastic nature of PnPDP solvers and the intrinsic uncertainty of inverse problems, critical for scientific tasks. This creates a fundamental mismatch: in inverse problems, the desired output is typically a posterior distribution and most PnPDP solvers induce a distribution over reconstructions, but existing benchmarks only evaluate a single reconstruction, ignoring distributional characterization such as uncertainty. To address this gap, we conduct a systematic study to benchmark the uncertainty quantification (UQ) of existing diffusion inverse solvers. Specifically, we design a rigorous toy model simulation to evaluate the uncertainty behavior of various PnPDP solvers, and propose a UQ-driven categorization. Through extensive experiments on toy simulations and diverse real-world scientific inverse problems, we observe uncertainty behaviors consistent with our taxonomy and theoretical justification, providing new insights for evaluating and understanding the uncertainty for PnPDPs.