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Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance.
Cortical feedback improves discrimination between figure and background by v1, v2 and v3 neurons
J. M. Hupé, A. C. James, B. R. Payne, S. G. Lomber, P Girard, and J Bullier · 1998
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David R. Martin, Charless C. Fowlkes, Doron Tal, and Jitendra Malik · 2001
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
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An edge-guided image interpolation algorithm via directional filtering and data fusion
Lei Zhang and Xiaolin Wu · 2006
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Brain states: top-down influences in sensory processing
Charles D Gilbert and Mariano Sigman · 2007
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 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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Single image super-resolution with non-local means and steering kernel regression
Kaibing Zhang, Xinbo Gao, Dacheng Tao, Xuelong Li, et al · 2012
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Anchored neighborhood regression for fast example-based super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 2013
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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A statistical prediction model based on sparse representations for single image super-resolution
Tomer Peleg and Michael Elad · 2014
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Curriculum learning of multiple tasks
Anastasia Pentina, Viktoriia Sharmanska, and Christoph H. Lampert · 2014
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Deep networks with internal selective attention through feedback connections
Marijn F Stollenga, Jonathan Masci, Faustino Gomez, and Jürgen Schmidhuber · 2014
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Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks
Chunshui Cao, Xianming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, and Thomas S. Huang · 2015
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Human pose estimation with iterative error feedback
Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, and Jitendra Malik · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
Cited alongside, same era.
Fast and accurate image upscaling with super-resolution forests
Samuel Schulter, Christian Leistner, and Horst Bischof · 2015
Cited alongside, same era.
A+: Adjusted anchored neighborhood regression for fast super-resolution
Radu Timofte, Vincent De Smet, and Luc Van Gool · 2015
Cited alongside, same era.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2016
Sketch-based manga retrieval using manga109 dataset
Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2017
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Image super-resolution via deep recursive residual network
Ying Tai, Jian Yang, and Xiaoming Liu · 2017
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Memnet: A persistent memory network for image restoration
Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu · 2017
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Image super-resolution using dense skip connections
Tong Tong, Gen Li, Xiejie Liu, and Qinquan Gao · 2017
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Feedback networks
Amir R. Zamir, Te-Lin Wu, Lin Sun, William B. Shen, Bertram E. Shi, Jitendra Malik, and Silvio Savarese · 2017
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Learning deep CNN denoiser prior for image restoration
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang · 2017
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Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Van Der Maaten Laurens, and Kilian Q Weinberger · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Cited alongside, same era.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Cited alongside, same era.
Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Qianli Liao and Tomaso Poggio · 2016
Cited alongside, same era.
Seven ways to improve example-based single image super resolution
Radu Timofte, Rasmus Rothe, and Luc Van Gool · 2016
Cited alongside, same era.
Learning a single convolutional super-resolution network for multiple degradations
Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2017
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Image super-resolution via dual-state recurrent networks
Wei Han, Shiyu Chang, Ding Liu, Mo Yu, Michael Witbrock, and Thomas S. Huang · 2018
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Deep back-projection networks for super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2018
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Fast and accurate single image super-resolution via information distillation network
Zheng Hui, Xiumei Wang, and Xinbo Gao · 2018
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Top-down feedback for crowd counting convolutional neural network
Deepak Babu Sam and R. Venkatesh Babu · 2018
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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
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A fully progressive approach to single-image super-resolution
Yifan Wang, Federico Perazzi, Brian Mcwilliams, Alexander Sorkinehornung, Olga Sorkinehornung, and Christopher Schroers · 2018
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Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
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Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
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