Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors

• URL: http://arxiv.org/abs/2403.11407v1
• Date: Mon, 18 Mar 2024 01:47:24 GMT
• Title: Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
• Authors: Yazid Janati, Alain Durmus, Eric Moulines, Jimmy Olsson,
• Abstract summary: In this work, we take a different approach 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.
• Score: 21.51814794909746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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