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A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
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 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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An edge-guided image interpolation algorithm via directional filtering and data fusion
L. Zhang and X. Wu · 2006
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On single image scale-up using sparse-representations
R. Zeyde, M. Elad, and M. Protter · 2010
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Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 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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Single image super-resolution with non-local means and steering kernel regression
K. Zhang, X. Gao, D. Tao, and X. Li · 2012
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Very low resolution face recognition problem
W. W. Zou and P. C. Yuen · 2012
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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
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Anchored neighborhood regression for fast example-based super-resolution
R. Timofte, V. De, and L. V. 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
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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A statistical prediction model based on sparse representations for single image super-resolution
T. Peleg and M. Elad · 2014
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A+: Adjusted anchored neighborhood regression for fast super-resolution
R. Timofte, V. De Smet, and L. Van Gool · 2014
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Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
Cited alongside, same era.
Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 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.
Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2016
Cited alongside, same era.
Accelerating the super-resolution convolutional neural network
C. Dong, C. C. Loy, and X. Tang · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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
Later among the works it cites.
Sketch-based manga retrieval using manga109 dataset
Y. Matsui, K. Ito, Y. Aramaki, A. Fujimoto, T. Ogawa, T. Yamasaki, and K. Aizawa · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Image super-resolution via deep recursive residual network
Y. Tai, J. Yang, and X. Liu · 2017
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Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
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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
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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.
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.
Seven ways to improve example-based single image super resolution
R. Timofte, R. Rothe, and L. Van Gool · 2016
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
Cited alongside, same era.
Later among the works it cites.
Image super-resolution using dense skip connections
T. Tong, G. Li, X. Liu, and Q. Gao · 2017
Later among the works it cites.
Image de-raining using a conditional generative adversarial network
H. Zhang, V. Sindagi, and V. M. Patel · 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.
Collaborative representation cascade for single image super-resolution
Y. Zhang, Y. Zhang, J. Zhang, D. Xu, Y. Fu, Y. Wang, X. Ji, and Q. Dai · 2017
Later among the works it cites.
Tell me where to look: Guided attention inference network
K. Li, Z. Wu, K.-C. Peng, J. Ernst, and Y. Fu · 2018
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Densely connected pyramid dehazing network
H. Zhang and V. M. Patel · 2018
Closest in time.
Density-aware single image de-raining using a multi-stream dense network
H. Zhang and V. M. Patel · 2018
Closest in time.
Learning a single convolutional super-resolution network for multiple degradations
K. Zhang, W. Zuo, and L. Zhang · 2018
Closest in time.