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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.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
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Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Image denoising and inpainting with deep neural networks
Junyuan Xie, Linli Xu, and Enhong Chen · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Image restoration using convolutional auto-encoders with symmetric skip connections
Xiao-Jiao Mao, Chunhua Shen, and Yu-Bin Yang · 2016
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Cited alongside, same era.
Benchmarking denoising algorithms with real photographs
Tobias Plotz and Stefan Roth · 2017
Cited alongside, same era.
High-resolution image inpainting using multi-scale neural patch synthesis
Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, and Hao Li · 2017
Cited alongside, same era.
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
Later among the works it cites.
Noise2noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila · 2018
Later among the works it cites.
Noise2self: Blind denoising by self-supervision
Joshua Batson and Loic Royer · 2019
Closest in time.
Noise2void-learning denoising from single noisy images
Alexander Krull, Tim-Oliver Buchholz, and Florian Jug · 2019
Closest in time.
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