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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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autoři: Bai, Weimin, Tang, Weiheng, Ye, Enze, Chen, Siyi, Chen, Wenzheng, Sun, He
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.04.2025
Témata:
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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Shrnutí: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 clean data is costly or impractical. Recent approaches have attempted to learn diffusion models directly from corrupted measurements, but these methods either lack theoretical convergence guarantees or are restricted to specific types of data corruption. In this paper, we propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types. Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images. This process alternates between estimation and training until convergence. We evaluate the performance of our method across various imaging tasks, including inpainting, denoising, and deblurring. Experimental results demonstrate that our approach enables the learning of high-fidelity diffusion priors from noisy data, significantly enhancing reconstruction quality in imaging inverse problems.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10889371