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Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images
[article]
2019
arXiv
pre-print
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Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training of deep neural network Gaussian denoisers that outperformed classical non-deep learning based denoisers and yielded ...
per image for Noise2Noise). ...
Stein's unbiased risk estimator (SURE) based training method for Gaussian denoisers was proposed to train DNNs with a set of a single noise realization per image, but it was limited to Gaussian denoising ...
arXiv:1902.02452v2
fatcat:7n354pswhredxkigyvpubbu54y
Noise2Self: Blind Denoising by Self-Supervision
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1901.11365v1 fatcat:sgdsfjln3zgx5ixqccjlwicjau
This framework generalizes recent work on training neural nets from noisy images and on cross-validation for matrix factorization. ...
We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. ...
Thank you to Jack Kamm for discussions on Gaussian Processes and shrinkage estimators. ...
arXiv:1901.11365v2
fatcat:n672sj7fung27dexfzxlx43zga