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Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration.
Denoising by soft-thresholding
David L. Donoho · 1995
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
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Multi-scale structural similarity for image quality assessment
Zhou Wang, Eero P. Simoncelli, and Alan C. Bovik · 2003
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A non-local algorithm for image denoising
Antoni Buades, Bartomeu Coll, and Jean-Michel Morel · 2005
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Neighbourhood components analysis
Jacob Goldberger, Geoffrey E. Hinton, Sam T. Roweis, and Ruslan R. Salakhutdinov · 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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Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data
Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian · 2008
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Natural image denoising with convolutional networks
Viren Jain and H. Sebastian Seung · 2008
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Fields of experts
Stefan Roth and Michael J. Black · 2009
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Distance metric learning for large margin nearest neighbor classification
Kilian Q. Weinberger and Lawrence K. Saul · 2009
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Image super-resolution via sparse representation
Jianchao Yang, John Wright, Thomas S. Huang, and Yi Ma · 2010
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Internal statistics of a single natural image
Maria Zontak and Michal Irani · 2011
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel · 2012
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Separating signal from noise using patch recurrence across scales
Maria Zontak, Inbar Mosseri, and Michal Irani · 2013
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What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Learning convolutional nonlinear features for k nearest neighbor image classification
Weiqiang Ren, Yinan Yu, Junge Zhang, and Kaiqi Huang · 2014
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Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
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Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Cited alongside, same era.
Needle-match: Reliable patch matching under high uncertainty
Or Lotan and Michal Irani · 2016
Cited alongside, same era.
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang · 2016
Cited alongside, same era.
Benchmarking denoising algorithms with real photographs
Tobias Plötz and Stefan Roth · 2017
Later among the works it cites.
The little engine that could: Regularization by denoising (RED)
Yaniv Romano, Michael Elad, and Peyman Milanfar · 2017
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MemNet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
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NTIRE 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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Variational inference for Monte Carlo objectives
Andriy Mnih and Danilo J. Rezende · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
Cited alongside, same era.
Ask, attend and answer: Exploring question-guided spatial attention for visual question answering
Huijuan Xu and Kate Saenko · 2016
Cited alongside, same era.
NTIRE 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Cited alongside, same era.
Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification
Woong Bae, Jae Jun Yoo, and Jong Chul Ye · 2017
Cited alongside, same era.
Deep mean-shift priors for image restoration
Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, and Meiguang Jin · 2017
Cited alongside, same era.
Later among the works it cites.
Learning deep CNN denoiser prior for image restoration
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang · 2017
Later among the works it cites.
Nonlocality-reinforced convolutional neural networks for image denoising
Cristóvão Cruz, Alessandro Foi, Vladimir Katkovnik, and Karen O. Egiazarian · 2018
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Single image super-resolution based on Wiener filter in similarity domain
Cristóvão Cruz, Rakesh Mehta, Vladimir Katkovnik, and Karen O. Egiazarian · 2018
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Toward convolutional blind denoising of real photographs
Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2018
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Universal denoising networks: A novel CNN-based network architecture for image denoising
Stamatios Lefkimmiatis · 2018
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Non-local recurrent network for image restoration
Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, and Thomas Huang · 2018
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Non-local neural networks
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A trilateral weighted sparse coding scheme for real-world image denoising
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BM3D-Net: A convolutional neural network for transform-domain collaborative filtering
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Learning to find good correspondences
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FFDNet: Toward a fast and flexible solution for CNN based image denoising
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