ML Anthology

Noisier2Noise: Learning to Denoise from Unpaired Noisy Data

Nick Moran, Dan Schmidt, Yu Zhong, Patrick Coady
CVPR 2020
doi:10.1109/CVPR42600.2020.01208 /cvpr/2020/moran2020cvpr-noisier2noise/

Abstract

We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise.

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Cite

Text

Moran et al. "Noisier2Noise: Learning to Denoise from Unpaired Noisy Data." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.01208

Markdown

[Moran et al. "Noisier2Noise: Learning to Denoise from Unpaired Noisy Data." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/moran2020cvpr-noisier2noise/) doi:10.1109/CVPR42600.2020.01208

BibTeX

@inproceedings{moran2020cvpr-noisier2noise,
  title     = {{Noisier2Noise: Learning to Denoise from Unpaired Noisy Data}},
  author    = {Moran, Nick and Schmidt, Dan and Zhong, Yu and Coady, Patrick},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.01208},
  url       = {https://mlanthology.org/cvpr/2020/moran2020cvpr-noisier2noise/}
}