  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.
