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Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration.
Total variation based image restoration with free local constraints
L. I. Rudin and S. Osher · 1994
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Bilateral filtering for gray and color images
C. Tomasi and R. Manduchi · 1998
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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
Earlier work this paper cites.
Super-resolution through neighbor embedding
H. Chang, D.-Y. Yeung, and Y. Xiong · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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A non-local algorithm for image denoising
A. Buades, B. Coll, and J.-M. Morel · 2005
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Image denoising via sparse and redundant representations over learned dictionaries
M. Elad and M. Aharon · 2006
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Image denoising by sparse 3-d transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2007
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Super-resolution from a single image
D. Glasner, S. Bagon, and M. Irani · 2009
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Non-local sparse models for image restoration
J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman · 2009
Earlier work this paper cites.
Image super-resolution via sparse representation
J. Yang, J. Wright, T. S. Huang, and Y. Ma · 2010
Earlier work this paper cites.
On single image scale-up using sparse-representations
R. Zeyde, M. Elad, and M. Protter · 2010
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Image and video upscaling from local self-examples
G. Freedman and R. Fattal · 2011
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From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel · 2012
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Image denoising: Can plain neural networks compete with bm3d?
H. C. Burger, C. J. Schuler, and S. Harmeling · 2012
Earlier work this paper cites.
Bm3d frames and variational image deblurring
A. Danielyan, V. Katkovnik, and K. Egiazarian · 2012
Earlier work this paper cites.
Anchored neighborhood regression for fast example-based super-resolution
R. Timofte, V. De, and L. Van Gool · 2013
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Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
Cited alongside, same era.
Weighted nuclear norm minimization with application to image denoising
S. Gu, L. Zhang, W. Zuo, and X. Feng · 2014
Cited alongside, same era.
Shrinkage fields for effective image restoration
U. Schmidt and S. Roth · 2014
Cited alongside, same era.
External patch prior guided internal clustering for image denoising
F. Chen, L. Zhang, and H. Yu · 2015
Cited alongside, same era.
Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
Cited alongside, same era.
Fast and accurate image upscaling with super-resolution forests
S. Schulter, C. Leistner, and H. Bischof · 2015
Cited alongside, same era.
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Y. Chen and T. Pock · 2017
Later among the works it cites.
Convolutional sequence to sequence learning
J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin · 2017
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Segmentation-aware convolutional networks using local attention masks
A. W. Harley, K. G. Derpanis, and I. Kokkinos · 2017
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Deep laplacian pyramid networks for fast and accurate super-resolution
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2017
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Non-local color image denoising with convolutional neural networks
S. Lefkimmiatis · 2017
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Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee · 2017
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Deep networks for image super-resolution with sparse prior
Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang · 2015
Cited alongside, same era.
Structured overcomplete sparsifying transform learning with convergence guarantees and applications
B. Wen, S. Ravishankar, and Y. Bresler · 2015
Cited alongside, same era.
Patch group based nonlocal self-similarity prior learning for image denoising
J. Xu, L. Zhang, W. Zuo, D. Zhang, and X. Feng · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr · 2015
Cited alongside, same era.
Accelerating the super-resolution convolutional neural network
C. Dong, C. C. Loy, and X. Tang · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Later among the works it cites.
Learning non-local image diffusion for image denoising
P. Qiao, Y. Dou, W. Feng, R. Li, and Y. Chen · 2017
Later among the works it cites.
A simple neural network module for relational reasoning
A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap · 2017
Later among the works it cites.
Image super-resolution via deep recursive residual network
Y. Tai, J. Yang, and X. Liu · 2017
Later among the works it cites.
Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
Later among the works it cites.
Image super-resolution using dense skip connections
T. Tong, G. Li, X. Liu, and Q. Gao · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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X. Wang, R. Girshick, A. Gupta, and K. He · 2017
Later among the works it cites.
A tale of two bases: Local-nonlocal regularization on image patches with convolution framelets
R. Yin, T. Gao, Y. M. Lu, and I. Daubechies · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
Later among the works it cites.
Learning deep cnn denoiser prior for image restoration
K. Zhang, W. Zuo, S. Gu, and L. Zhang · 2017
Later among the works it cites.
Image super-resolution via dual-state recurrent networks
W. Han, S. Chang, D. Liu, M. Yu, M. Witbrock, and T. S. Huang · 2018
Closest in time.
When image denoising meets high-level vision tasks: A deep learning approach
D. Liu, B. Wen, X. Liu, Z. Wang, and T. S. Huang · 2018
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