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Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks.
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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A non-local algorithm for image denoising
Antoni Buades, Bartomeu Coll, and J-M Morel · 2005
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Image denoising with block-matching and 3d filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2006
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Fields of experts
Stefan Roth and Michael J Black · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Shrinkage fields for effective image restoration
Uwe Schmidt and Stefan Roth · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Yunjin Chen and Thomas Pock · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi · 2016
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 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
Cited alongside, same era.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Noise2noise
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila, et al · 2018
Cited alongside, same era.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Noise2void-learning denoising from single noisy images
Alexander Krull, Tim-Oliver Buchholz, and Florian Jug · 2019
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Probabilistic noise2void: Unsupervised content-aware denoising
Alexander Krull, Tomas Vicar, and Florian Jug · 2019
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High-quality self-supervised deep image denoising
Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila · 2019
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Noisier2noise: Learning to denoise from unpaired noisy data
Nick Moran, Dan Schmidt, Yu Zhong, and Patrick Coady · 2019
Later among the works it cites.
Noisy-as-clean: learning unsupervised denoising from the corrupted image
Jun Xu, Yuan Huang, Li Liu, Fan Zhu, Xingsong Hou, and Ling Shao · 2019
Later among the works it cites.
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Cited alongside, same era.
Content-aware image restoration: pushing the limits of fluorescence microscopy
Martin Weigert, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, Deborah Schmidt, Coleman Broaddus, Siân Culley, et al · 2018
Cited alongside, same era.
Noise2self: Blind denoising by self-supervision
Joshua Batson and Loïc Royer · 2019
Cited alongside, same era.
Unprocessing images for learned raw denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron · 2019
Cited alongside, same era.
Toward convolutional blind denoising of real photographs
Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2019
Cited alongside, same era.
Global pixel transformers for virtual staining of microscopy images
Yi Liu, Hao Yuan, Zhengyang Wang, and Shuiwang Ji · 2020
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Leveraging self-supervised denoising for image segmentation
Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit, Pavel Tomancak, Florian Jug, and Alexander Krull · 2020
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Global voxel transformer networks for augmented microscopy
Zhengyang Wang, Yaochen Xie, and Shuiwang Ji · 2020
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Non-local u-nets for biomedical image segmentation
Zhengyang Wang, Na Zou, Dinggang Shen, and Shuiwang Ji · 2020
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Residual dense network for image restoration
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2020
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