Stochastic Generative Plug-and-Play Priors

2026-04-04

AI Summary

This work addresses the challenge of effectively leveraging score-based diffusion models as priors within the Plug-and-Play (PnP) framework. The authors propose a stochastic generative PnP method that establishes, for the first time, a theoretical connection between PnP and diffusion models from a score-matching perspective—without relying on reverse diffusion sampling. By incorporating a noise injection mechanism, the approach is equivalent to optimizing a Gaussian-smoothed objective, which enhances robustness in severely ill-posed inverse problems and facilitates escape from strict saddle points. Experimental results demonstrate that the proposed method significantly outperforms conventional PnP approaches in multi-coil MRI reconstruction and large-mask image inpainting, achieving performance comparable to state-of-the-art diffusion-based solvers.

Abstract

Plug-and-play (PnP) methods are widely used for solving imaging inverse problems by incorporating a denoiser into optimization algorithms. Score-based diffusion models (SBDMs) have recently demonstrated strong generative performance through a denoiser trained across a wide range of noise levels. Despite their shared reliance on denoisers, it remains unclear how to systematically use SBDMs as priors within the PnP framework without relying on reverse diffusion sampling. In this paper, we establish a score-based interpretation of PnP that justifies using pretrained SBDMs directly within PnP algorithms. Building on this connection, we introduce a stochastic generative PnP (SGPnP) framework that injects noise to better leverage the expressive generative SBDM priors, thereby improving robustness in severely ill-posed inverse problems. We provide a new theory showing that this noise injection induces optimization on a Gaussian-smoothed objective and promotes escape from strict saddle points. Experiments on challenging inverse tasks, such as multi-coil MRI reconstruction and large-mask natural image inpainting, demonstrate consistent improvement over conventional PnP methods and achieve performance competitive with diffusion-based solvers.

Problem

• Plug-and-Play
• Score-based Diffusion Models
• Inverse Problems
• Generative Priors
• Ill-posed Imaging

Innovation

• score-based diffusion models
• plug-and-play priors
• stochastic optimization
• inverse problems
• generative priors