An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

Weimin Bai; Yifei Wang; Wenzheng Chen; He Sun

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arXiv · 2407.01014

An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations

Weimin Bai·Yifei Wang·Wenzheng Chen·He Sun

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Abstract

Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits their practical use where clean data is scarce. In this paper, we propose EMDiffusion, an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean images from corrupted data using a known diffusion model (E-step) and refining diffusion model weights based on these reconstructions (M-step). This iterative process leads the learned diffusion model to gradually converge to the true clean data distribution. We validate our method through extensive experiments on diverse computational imaging tasks, including random inpainting, denoising, and deblurring, achieving new state-of-the-art performance.

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Weimin Bai, Yifei Wang, Wenzheng Chen, He Sun. 2024-07-01. An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations. https://arxiv.org/abs/2407.01014

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