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The primary aim of single-image super-resolution is to construct high-resolution (HR) images from corresponding low-resolution (LR) inputs.
Limits on super-resolution and how to break them
Simon Baker and Takeo Kanade · 2000
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Making a “completely blind” image quality analyzer
A. Mittal, R. Soundararajan, and A. C. Bovik · 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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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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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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A comprehensive survey to face hallucination
Nannan Wang, Dacheng Tao, Xinbo Gao, Xuelong Li, and Jie Li · 2014
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Multi-frame example-based super-resolution using locally directional self-similarity
Seokhwa Jeong, Inhye Yoon, and Joonki Paik · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Accurate image super-resolution using very deep convolutional networks
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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Super resolution applications in modern digital image processing
Amanjot Singh and Jagroop Singh Sidhu · 2016
Cited alongside, same era.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G. Dimakis · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Cited alongside, same era.
New techniques for preserving global structure and denoising with low information loss in single-image super-resolution
Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, and Cynthia Rudin · 2018
Cited alongside, same era.
The 2018 PIRM challenge on perceptual image super-resolution
Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, and Lihi Zelnik-Manor · 2018
Cited alongside, same era.
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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Unsupervised degradation learning for single image super-resolution
Tianyu Zhao, Changqing Zhang, Wenqi Ren, Dongwei Ren, and Qinghua Hu · 2018
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Image2StyleGAN: How to embed images into the StyleGAN latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Progressive face super-resolution via attention to facial landmark
Deokyun Kim, Minseon Kim, Gihyun Kwon, and Dae-Shik Kim · 2019
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To learn image super-resolution, use a gan to learn how to do image degradation first
Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos · 2018
Cited alongside, same era.
Fsrnet: End-to-end learning face super-resolution with facial priors
Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, and Jian Yang · 2018
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Cited alongside, same era.
Random Vectors in High Dimensions
Roman Vershynin · 2018
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al
Cited in the paper.
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Pytorch implementation of the stylegan generator
Thomas Viehmann and Lernapparat · 2019
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Fairgan+: Achieving fair data generation and classification through generative adversarial nets
D. Xu, S. Yuan, L. Zhang, and X. Wu · 2019
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Analyzing demographic bias in artificially generated facial pictures
Joni Salminen, Soon-gyo Jung, Shammur Chowdhury, and Bernard J. Jansen · 2020
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