Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method

Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clean datasets for training limits their applicability in scenarios where acquiring...

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Vydané v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autori:	Bai, Weimin, Tang, Weiheng, Ye, Enze, Chen, Siyi, Chen, Wenzheng, Sun, He
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 06.04.2025
Predmet:	Acoustic measurements Bayesian inference Computational imaging Convergence Diffusion models image priors Image reconstruction Imaging Inverse problems Noise measurement Signal processing algorithms Speech processing Training
ISSN:	2379-190X
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