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Recent research on super-resolution has achieved great success due to the development of deep convolutional neural networks (DCNNs).
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
Earlier work this paper cites.
On single image scale-up using sparse-representations
R. Zeyde, M. Elad, and M. Protter · 2010
Earlier work this paper cites.
A novel image fusion method using ikonos satellite images
D. Yıldırım and O. Güngör · 2012
Earlier work this paper cites.
Very low resolution face recognition problem
W. W. Zou and P. C. Yuen · 2012
Earlier work this paper cites.
Cardiac image super-resolution with global correspondence using multi-atlas patchmatch
W. Shi, J. Caballero, C. Ledig, X. Zhuang, W. Bai, K. Bhatia, A. M. S. M. de Marvao, T. Dawes, D. O’Regan, and D. Rueckert · 2013
Earlier work this paper cites.
Anchored neighborhood regression for fast example-based super-resolution
R. Timofte, V. De Smet, and L. Van Gool · 2013
Earlier work this paper cites.
Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
Earlier work this paper cites.
Metalearning: a survey of trends and technologies
C. Lemke, M. Budka, and B. Gabrys · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
Deeply-recursive convolutional network for image super-resolution
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
Later among the works it cites.
Ntire 2017 challenge on single image super-resolution: Methods and results
R. Timofte, E. Agustsson, L. Van Gool, M.-H. Yang, L. Zhang, B. Lim, S. Son, H. Kim, S. Nah, K. M. Lee, et al · 2017
Later among the works it cites.
Memory matching networks for one-shot image recognition
Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei · 2018
Later among the works it cites.
Decouple learning for parameterized image operators
Q. Fan, D. Chen, L. Yuan, G. Hua, N. Yu, and B. Chen · 2018
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
Later among the works it cites.
Deep back-projection networks for super-resolution
M. Haris, G. Shakhnarovich, and N. Ukita · 2018
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
Cited alongside, same era.
Learning to learn: Model regression networks for easy small sample learning
Y.-X. Wang and M. Hebert · 2016
Cited alongside, same era.
Learning to segment every thing
R. Hu, P. Dollár, K. He, T. Darrell, and R. Girshick · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Cited alongside, same era.
Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee · 2017
Cited alongside, same era.
Image super-resolution via deep recursive residual network
Y. Tai, J. Yang, and X. Liu · 2017
Cited alongside, same era.
Later among the works it cites.
Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation
Y. Jo, S. Wug Oh, J. Kang, and S. Joo Kim · 2018
Later among the works it cites.
Recovering realistic texture in image super-resolution by deep spatial feature transform
X. Wang, K. Yu, C. Dong, and C. Change Loy · 2018
Later among the works it cites.
Metaanchor: Learning to detect objects with customized anchors
T. Yang, X. Zhang, W. Zhang, and J. Sun · 2018
Later among the works it cites.
Learning a single convolutional super-resolution network for multiple degradations
K. Zhang, W. Zuo, and L. Zhang · 2018
Later among the works it cites.
Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
Later among the works it cites.
Residual dense network for image super-resolution
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu · 2018
Later among the works it cites.