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Convolutional neural networks are the most successful models in single image super-resolution.
An edge-guided image interpolation algorithm via directional filtering and data fusion
L. Zhang and X. Wu · 2006
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Contour detection and hierarchical image segmentation
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik · 2010
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.
Low-complexity single-image super-resolution based on nonnegative neighbor embedding
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel · 2012
Earlier work this paper cites.
Single image super-resolution with non-local means and steering kernel regression
K. Zhang, X. Gao, D. Tao, and X. Li · 2012
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.
A statistical prediction model based on sparse representations for single image super-resolution
T. Peleg and M. Elad · 2014
Earlier work this paper cites.
Accelerating the super-resolution convolutional neural network
C. Dong, C. C. Loy, and X. Tang · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Do deep convolutional nets really need to be deep and convolutional?
G. Urban, K. J. Geras, S. E. Kahou, O. Aslan, S. Wang, R. Caruana, A. Mohamed, M. Philipose, and M. Richardson · 2016
Earlier work this paper cites.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Earlier work this paper cites.
Deep laplacian pyramid networks for fast and accurate super-resolution
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
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Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Image super-resolution using dense skip connections
T. Tong, G. Li, X. Liu, and Q. Gao · 2017
Cited alongside, same era.
Learning deep cnn denoiser prior for image restoration
K. Zhang, W. Zuo, S. Gu, and L. Zhang · 2017
Cited alongside, same era.
Fast, accurate, and lightweight super-resolution with cascading residual network
N. Ahn, B. Kang, and K.-A. Sohn · 2018
Cited alongside, same era.
Lightweight and efficient image super-resolution with block state-based recursive network
Fast, accurate and lightweight super-resolution with neural architecture search
X. Chu, B. Zhang, H. Ma, R. Xu, J. Li, and Q. Li · 2019
Later among the works it cites.
Second-order attention network for single image super-resolution
T. Dai, J. Cai, Y. Zhang, S.-T. Xia, and L. Zhang · 2019
Later among the works it cites.
Lightweight image super-resolution with information multi-distillation network
Z. Hui, X. Gao, Y. Yang, and X. Wang · 2019
Later among the works it cites.
Feedback network for image super-resolution
Z. Li, J. Yang, Z. Liu, X. Yang, G. Jeon, and W. Wu · 2019
Later among the works it cites.
F. Zhu and Q. Zhao · 2019
Later among the works it cites.
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J.-H. Choi, J.-H. Kim, M. Cheon, and J.-S. Lee · 2018
Cited alongside, same era.
Deep back-projection networks for super-resolution
M. Haris, G. Shakhnarovich, and N. Ukita · 2018
Cited alongside, same era.
Cascaded deep networks with multiple receptive fields for infrared image super-resolution
Z. He, S. Tang, J. Yang, Y. Cao, M. Y. Yang, and Y. Cao · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Cited alongside, same era.
Fast and accurate single image super-resolution via information distillation network
Z. Hui, X. Wang, and X. Gao · 2018
Cited alongside, same era.
Attend and rectify: a gated attention mechanism for fine-grained recovery
P. Rodríguez, J. M. Gonfaus, G. Cucurull, F. XavierRoca, and J. Gonzalez · 2018
Cited alongside, same era.
A fully progressive approach to single-image super-resolution
Y. Wang, F. Perazzi, B. McWilliams, A. Sorkine-Hornung, O. Sorkine-Hornung, and C. Schroers · 2018
Cited alongside, same era.
Residual feature aggregation network for image super-resolution
J. Liu, W. Zhang, Y. Tang, J. Tang, and G. Wu · 2020
Closest in time.
Mprnet: Multi-path residual network for lightweight image super resolution
A. Mehri, P. B. Ardakani, and A. D. Sappa · 2020
Closest in time.
Multi-attention based ultra lightweight image super-resolution
A. Muqeet, J. Hwang, S. Yang, J. Kang, Y. Kim, and S.-H. Bae · 2020
Closest in time.
Single image super-resolution via a holistic attention network
B. Niu, W. Wen, W. Ren, X. Zhang, L. Yang, S. Wang, K. Zhang, X. Cao, and H. Shen · 2020
Closest in time.
Eca-net: Efficient channel attention for deep convolutional neural networks
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu · 2020
Closest in time.
Efficient image super-resolution using pixel attention
H. Zhao, X. Kong, J. He, Y. Qiao, and C. Dong · 2020
Closest in time.
Overnet: Lightweight multi-scale super-resolution with overscaling network
P. Behjati, P. Rodriguez, A. Mehri, I. Hupont, C. F. Tena, and J. Gonzalez · 2021
Closest in time.