Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we propose GibbsDDRM, an extension of Denoising Diffusion Restoration Models (DDRM) to a blind setting in which the linear measurement operator is unknown. GibbsDDRM constructs a joint distribution of the data, measurements, and linear operator by using a pre-trained diffusion model for the data prior, and it solves the problem by posterior sampling with an efficient variant of a Gibbs sampler. The proposed method is problem-agnostic, meaning that a pre-trained diffusion model can be applied to various inverse problems without fine tuning. In experiments, it achieved high performance on both blind image deblurring and vocal dereverberation tasks, despite the use of simple generic priors for the underlying linear operators.

本文利用预训练扩散模型，在不知道线性运算符的盲设置中推出了GibbsDDRM，并通过后验采样解决问题。GibbsDDRM可应用于各种反问题，即使使用简单的通用先验，也能在盲图像去模糊和音频去混响任务中实现高性能。

GibbsDDRM: 利用分布式采样器求解带去噪扩散还原的盲反问题