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arXiv · 2305.16482

Score-based Diffusion Models for Bayesian Image Reconstruction

Michael T. McCann·Hyungjin Chung·Jong Chul Ye·Marc L. Klasky
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Abstract

This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can also be used for solving image reconstruction problems. We present a simple and flexible algorithm for training a diffusion model and using it for maximum a posteriori reconstruction, minimum mean square error reconstruction, and posterior sampling. We present experiments on both a linear and a nonlinear reconstruction problem that highlight the strengths and limitations of the approach.

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Michael T. McCann, Hyungjin Chung, Jong Chul Ye, Marc L. Klasky. 2023-05-25. Score-based Diffusion Models for Bayesian Image Reconstruction. https://doi.org/10.1109/icip49359.2023.10222481

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