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The extraction and proper utilization of convolution neural network (CNN) features have a significant impact on the performance of image super-resolution (SR).
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, E. P. Simoncelli, et al · 2004
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Image super-resolution via sparse representation
J. Yang, J. Wright, T. S. Huang, and Y. Ma · 2010
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On single image scale-up using sparse-representations
R. Zeyde, M. Elad, and M. Protter · 2010
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
M. Bevilacqua, A. Roumy, C. Guillemot, and M. Alberi-Morel · 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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A statistical prediction model based on sparse representations for single image super-resolution
T. Peleg and M. Elad · 2014
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Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
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Hyperspectral super-resolution by coupled spectral unmixing
C. Lanaras, E. Baltsavias, and K. Schindler · 2015
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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
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Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2016
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Accelerating the super-resolution convolutional neural network
C. Dong, C. C. Loy, and X. Tang · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Accurate image super-resolution using very deep convolutional networks
J. Kim, J. K. Lee, and K. M. Lee · 2016
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Deeply-recursive convolutional network for image super-resolution
J. Kim, J. K. Lee, and K. M. 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.
A learned representation for artistic style
V. Dumoulin, J. Shlens, and M. Kudlur · 2017
Cited alongside, same era.
Deep laplacian pyramid networks for fast and accurate super-resolution
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2017
Cited alongside, same era.
Fast and accurate image super-resolution with deep laplacian pyramid networks
Image super-resolution using dense skip connections
T. Tong, G. Li, X. Liu, and Q. Gao · 2017
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Dilated residual networks
F. Yu, V. Koltun, and T. Funkhouser · 2017
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Learning deep cnn denoiser prior for image restoration
K. Zhang, W. Zuo, S. Gu, and L. Zhang · 2017
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Big-little net: An efficient multi-scale feature representation for visual and speech recognition
C.-F. Chen, Q. Fan, N. Mallinar, T. Sercu, and R. Feris · 2018
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Deep back-projection networks for super-resolution
M. Haris, G. Shakhnarovich, and N. Ukita · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 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. 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.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Cited alongside, same era.
Sketch-based manga retrieval using manga109 dataset
Y. Matsui, K. Ito, Y. Aramaki, A. Fujimoto, T. Ogawa, T. Yamasaki, and K. Aizawa · 2017
Cited alongside, same era.
Enhancenet: Single image super-resolution through automated texture synthesis
M. S. Sajjadi, B. Scholkopf, and M. Hirsch · 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.
Multi-scale residual network for image super-resolution
J. Li, F. Fang, K. Mei, and G. Zhang · 2018
Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Feature pyramid network for multi-class land segmentation
S. Seferbekov, V. Iglovikov, A. Buslaev, and A. Shvets · 2018
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Feature super-resolution: Make machine see more clearly
W. Tan, B. Yan, and B. Bare · 2018
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Learning a single convolutional super-resolution network for multiple degradations
K. Zhang, W. Zuo, and L. Zhang · 2018
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Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
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Residual dense network for image super-resolution
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu · 2018
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Dcsr: Dilated convolutions for single image super-resolution
Z. Zhang, X. Wang, and C. Jung · 2019
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