Plug-and-Play Posterior Sampling for Blind Inverse Problems
This addresses the problem of solving blind inverse problems in imaging for researchers and practitioners, offering a novel method that is incremental in its integration of diffusion models.
The paper tackles blind inverse problems where both the target image and measurement operator are unknown by introducing Blind-PnPDM, a framework that uses diffusion models for posterior sampling, and it outperforms state-of-the-art methods in blind image deblurring with improved quantitative metrics and visual fidelity.
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.