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Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly.
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
Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli, et al · 2004
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Super-resolution of human face image using canonical correlation analysis
Hua Huang, Huiting He, Xin Fan, and Junping Zhang · 2010
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Image super-resolution via sparse representation
Jianchao Yang, John Wright, Thomas S Huang, and Yi Ma · 2010
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie line Alberi Morel · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Texture synthesis using convolutional neural networks
Leon Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Earlier work this paper cites.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
Cited alongside, same era.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2017
Cited alongside, same era.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Multi–scale recursive and perception–distortion controllable image super–resolution
Pablo Navarrete Michelini, Hanwen Liu, and Dan Zhu · 2018
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Super-resolution for biometrics: A comprehensive survey
Kien Nguyen, Clinton Fookes, Sridha Sridharan, Massimo Tistarelli, and Mark Nixon · 2018
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Nima: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
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Analyzing perception-distortion tradeoff using enhanced perceptual super-resolution network
Subeesh Vasu, Nimisha Thekke Madam, and AN Rajagopalan · 2018
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Fast and efficient image quality enhancement via desubpixel convolutional neural networks
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Cited alongside, same era.
Learning a no-reference quality metric for single-image super-resolution
Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang · 2017
Cited alongside, same era.
Sketch-based manga retrieval using manga109 dataset
Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2017
Cited alongside, same era.
Enhancenet: Single image super-resolution through automated texture synthesis
Mehdi SM Sajjadi, Bernhard Schölkopf, and Michael Hirsch · 2017
Cited alongside, same era.
Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
Cited alongside, same era.
Image super-resolution using dense skip connections
Tong Tong, Gen Li, Xiejie Liu, and Qinquan Gao · 2017
Cited alongside, same era.
Ensemble based deep networks for image super-resolution
Lingfeng Wang, Zehao Huang, Yongchao Gong, and Chunhong Pan · 2017
Cited alongside, same era.
Fast, accurate, and lightweight super-resolution with cascading residual network
Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 2018
Cited alongside, same era.
Thang Vu, Cao Van Nguyen, Trung X Pham, Tung M Luu, and Chang D Yoo · 2018
Later among the works it cites.
The unreasonable effectiveness of texture transfer for single image super-resolution
Muhammad Waleed Gondal, Bernhard Scholkopf, and Michael Hirsch · 2018
Later among the works it cites.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Later among the works it cites.
A fully progressive approach to single-image super-resolution
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, and Christopher Schroers · 2018
Later among the works it cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Example-based image super-resolution via blur kernel estimation and variational reconstruction
Qi Yang, Yanzhu Zhang, and Tiebiao Zhao · 2019
Closest in time.
Aim 2019 challenge on constrained super-resolution: Methods and results
Kai Zhang, Shuhang Gu, Radu Timofte, Zheng Hui, Xiumei Wang, Xinbo Gao, Dongliang Xiong, Shuai Liu, Ruipeng Gang, Nan Nan, et al · 2019
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Ranksrgan: Generative adversarial networks with ranker for image super-resolution
Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao · 2019
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Binary neural networks: A survey
Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, and Nicu Sebe · 2020
Closest in time.
Glean: Generative latent bank for large-factor image super-resolution
Kelvin CK Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, and Chen Change Loy · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
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Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021
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Designing a practical degradation model for deep blind image super-resolution
Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte · 2021
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