Plug-and-Play Posterior Sampling for Blind Inverse Problems

TL;DR

This paper presents Blind-PnPDM, a novel diffusion-based framework for solving blind inverse problems by sampling from the posterior, outperforming existing methods in blind image deblurring tasks.

Contribution

It introduces a new approach that uses diffusion models as priors for both images and measurement parameters, enabling flexible posterior sampling in blind inverse problems.

Findings

Outperforms state-of-the-art in blind image deblurring

Effective posterior sampling with diffusion priors

Flexible framework adaptable to various inverse problems

Abstract

We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are unknown. Unlike conventional methods that rely on explicit priors or separate parameter estimation, our approach performs posterior sampling by recasting the problem into an alternating Gaussian denoising scheme. We leverage two diffusion models as learned priors: one to capture the distribution of the target image and another to characterize the parameters of the measurement operator. This PnP integration of diffusion models ensures flexibility and ease of adaptation. Our experiments on blind image deblurring show that Blind-PnPDM outperforms state-of-the-art methods in terms of both quantitative metrics and visual fidelity. Our results highlight the effectiveness of treating blind inverse problems as a sequence of denoising subproblems while harnessing the expressive power of diffusion-based priors.