Interest in the use of Denoising Diffusion Models (DDM) as priors for solving inverse Bayesian problems has recently increased significantly. However, sampling from the resulting posterior distribution poses a challenge. To solve this problem, previous works have proposed approximations to bias the drift term of the diffusion. In this work, we take a different approach and utilize the specific structure of the DDM prior to define a set of intermediate and simpler posterior sampling problems, resulting in a lower approximation error compared to previous methods. We empirically demonstrate the reconstruction capability of our method for general linear inverse problems using synthetic examples and various image restoration tasks.

翻译：近年来，将去噪扩散模型作为求解逆贝叶斯问题先验的兴趣显著增加。然而，从由此产生的后验分布中采样仍具挑战性。为解决此问题，先前研究提出了对扩散漂移项进行近似的方案。本研究提出不同方法，利用去噪扩散模型先验的特定结构定义一系列中间且更简单的后验采样问题，相比先前方法降低了近似误差。我们通过合成示例及多种图像恢复任务，实证展示了该方法在一般线性逆问题中的重建能力。