Noise2Noise: Learning image restoration without clean data

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openAccess
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Conference article in proceedings
Date
2018-01-01
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Language
en
Pages
12
4620-4631
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35th International Conference on Machine Learning, ICML 2018, Volume 7, Proceedings of Machine Learning Research, issue 80
Abstract
We apply basic statistical reasoning to signal reconstruction by machine learning - learning to map corrupted observations to clean signals - with a simple and powerful conclusion: It is possible to learn to restore images by only looking at corrupted examples, at performance at and some-times exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denois- ing synthetic Monte Carlo images, and reconstruction of undersampled MRI scans - all corrupted by different processes - based on noisy data only.
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Citation
Lehtinen, J, Munkberg, J, Hasselgren, J, Laine, S, Karras, T, Aittala, M & Aila, T 2018, Noise2Noise : Learning image restoration without clean data . in J Dy & A Krause (eds), 35th International Conference on Machine Learning, ICML 2018 . vol. 7, Proceedings of Machine Learning Research, no. 80, International Machine Learning Society, pp. 4620-4631, International Conference on Machine Learning, Stockholm, Sweden, 10/07/2018 .