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Normalization like Batch Normalization (BN) is a milestone technique to normalize the distributions of intermediate layers in deep learning, enabling faster training and better generalization accuracy.
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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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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Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
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Adam: A method for stochastic optimization
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
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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 Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Instance normalization: The missing ingredient for fast stylization
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Densely connected convolutional networks
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Deep laplacian pyramid networks for fast and accurate super-resolution
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2017
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Enhanced deep residual networks for single image super-resolution
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Sketch-based manga retrieval using manga109 dataset
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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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NTIRE 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, et al · 2017
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Feedback network for image super-resolution
Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, and Wei Wu · 2019
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Closed-loop matters: Dual regression networks for single image super-resolution
Yong Guo, Jian Chen, Jingdong Wang, Qi Chen, Jiezhang Cao, Zeshuai Deng, Yanwu Xu, and Mingkui Tan · 2020
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Learning with privileged information for efficient image super-resolution
Wonkyung Lee, Junghyup Lee, Dohyung Kim, and Bumsub Ham · 2020
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Residual feature distillation network for lightweight image super-resolution
Jie Liu, Jie Tang, and Gangshan Wu · 2020
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Residual feature aggregation network for image super-resolution
Jie Liu, Wenjie Zhang, Yuting Tang, Jie Tang, and Gangshan Wu · 2020
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Memory recursive network for single image super-resolution
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Attention is all you need
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Fast, accurate, and lightweight super-resolution with cascading residual network
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Deep back-projection networks for super-resolution
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Non-local recurrent network for image restoration
Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, and Thomas S. Huang · 2018
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ESRGAN: enhanced super-resolution generative adversarial networks
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Learning a single convolutional super-resolution network for multiple degradations
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Jie Liu, Minqiang Zou, Jie Tang, and Gangshan Wu · 2020
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Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining
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Single image super-resolution via a holistic attention network
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Cross-scale internal graph neural network for image super-resolution
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, and Chen Change Loy · 2020
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Pre-trained image processing transformer
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Learning a single network for scale-arbitrary super-resolution
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Context reasoning attention network for image super-resolution
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