Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling

TL;DR

This paper introduces CA-DPS, a covariance-aware method for diffusion models that improves inverse problem reconstruction by accurately approximating the reverse process covariance without extra training.

Contribution

It derives a closed-form covariance for the reverse diffusion process and proposes a finite difference approximation, enhancing posterior sampling in diffusion models.

Findings

Significant improvement in reconstruction quality.

No additional hyperparameter tuning needed.

Compatible with pretrained DDPMs.

Abstract

Inverse problems exist in many disciplines of science and engineering. In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectively modeled as inverse problems. Recently, denoising diffusion probabilistic models (DDPMs) are shown to provide a promising solution to noisy linear inverse problems without the need for additional task specific training. Specifically, with the prior provided by DDPMs, one can sample from the posterior by approximating the likelihood. In the literature, approximations of the likelihood are often based on the mean of conditional densities of the reverse process, which can be obtained using Tweedie formula. To obtain a better approximation to the likelihood, in this paper we first derive a closed form formula for the covariance of the reverse process. Then, we propose a method based on finite difference method to approximate this covariance such that it can be readily obtained from the existing pretrained DDPMs, thereby not increasing the complexity compared to existing approaches. Finally, based on the mean and approximated covariance of the reverse process, we present a new approximation to the likelihood. We refer to this method as covariance-aware diffusion posterior sampling (CA-DPS). Experimental results show that CA-DPS significantly improves reconstruction performance without requiring hyperparameter tuning. The code for the paper is put in the supplementary materials.