Awesome Super-Resolution

A curated list of awesome super-resolution resources.

Recently we released Deep Learning for Image Super-resolution: A Survey to the community. In this survey, we review this task on different aspects including problem statement, datasets, evaluation metrics, methodology, and domain-specific applications. Specifically, we decompose the state-of-the-art models into basic components (e.g., network design principles, learning strategies, etc), analyze these components hierarchically and further identify their advantages and limitations. We also raise some open issues and potential development directions in this field at the end of the survey.

After completing this survey, we decided to release the collected SR resources, hoping to push the development of the community. We will keep updating our survey and this SR resource collection. If you have any questions or suggestions, please feel free to contact us.

Table of Contents

1. Non-deep Learning based SR
2. Supervised SR
3. Unsupervised SR
4. SR Datasets
5. SR Metrics
6. Survey Resources
7. Other Resources

Citing this work

If this repository is helpful to you, please cite our survey.

@article{wang2019deep,
    title={Deep learning for image super-resolution: A survey},
    author={Wang, Zhihao and Chen, Jian and Hoi, Steven CH},
    journal={arXiv preprint arXiv:1902.06068},
    year={2019}
}

1. Non-deep Learning based SR

2013 ICCV

Anchored Neighborhood Regression for Fast Example-Based Super-Resolution, Timofte, Radu; De, Vincent; Van Gool, Luc, [OpenAccess], [Project], ANR, GR
Nonparametric Blind Super-resolution, Michaeli, Tomer; Irani, Michal, [OpenAccess], [Project], BlindSR

2014 ACCV

A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, Timofte, Radu; De Smet, Vincent; Van Gool, Luc, [ACCV], [Project], A+

2014 TIP

A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, Peleg, Tomer; Elad, Michael, [Matlab*], [TIP]

2015 CVPR

Fast and accurate image upscaling with super-resolution forests, Schulter, Samuel; Leistner, Christian; Bischof, Horst, [OpenAccess], RFL
Handling motion blur in multi-frame super-resolution, Ma, Ziyang; Liao, Renjie; Tao, Xin; Xu, Li; Jia, Jiaya; Wu, Enhua, [OpenAccess], [Project]
Single image super-resolution from transformed self-exemplars, Huang, Jia-Bin; Singh, Abhishek; Ahuja, Narendra, [Matlab*], [OpenAccess], SelfExSR, Urban100

2015 ICCV

Naive Bayes Super-Resolution Forest, Salvador, Jordi; Perez-Pellitero, Eduardo, [OpenAccess], [Project], NBSRF

2016 CVPR

PSyCo: Manifold Span Reduction for Super Resolution, Perez-Pellitero, Eduardo; Salvador, Jordi; Ruiz-Hidalgo, Javier; Rosenhahn, Bodo, [C++/Matlab*], [OpenAccess], PSyCo

2016 NIPS

Learning Parametric Sparse Models for Image Super-Resolution, Li, Yongbo; Dong, Weisheng; Xie, Xuemei; Shi, GUANGMING; Li, Xin; Xu, Donglai, [NIPS]

2017 CVIU

Learning a no-reference quality metric for single-image super-resolution, Ma, Chao; Yang, Chih-Yuan; Yang, Xiaokang; Yang, Ming-Hsuan, [arXiv], [CVIU], [Matlab*], [Project], Ma

2017 CVPR

Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding, Huang, Yawen; Shao, Ling; Frangi, Alejandro F., [arXiv], [OpenAccess], WEENIE

2017 CVPRW

SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests, Huang, Jun-Jie; Liu, Tianrui; Dragotti, Pier Luigi; Stathaki, Tania, [OpenAccess], SRHRF+

2017 ICLR

Amortised MAP Inference for Image Super-resolution, Sønderby, Casper Kaae; Caballero, Jose; Theis, Lucas; Shi, Wenzhe; Huszár, Ferenc, [arXiv], [OpenReview], AffGAN

2017 TCI

RAISR: Rapid and Accurate Image Super Resolution, Romano, Yaniv; Isidoro, John; Milanfar, Peyman, [arXiv], [TCI], RAISR

2018 CVPR

Fight Ill-Posedness with Ill-Posedness: Single-shot Variational Depth Super-Resolution from Shading, Haefner, Bjoern; Queau, Yvain; Mollenhoff, Thomas; Cremers, Daniel, [Matlab*], [OpenAccess]

2018 IJCV

Hallucinating Compressed Face Images, Yang, Chih-Yuan; Liu, Sifei; Yang, Ming-Hsuan, [IJCV], [Matlab*], [Project], FHCI

2. Supervised SR

2.1 Generic Image SR

2014 ECCV

Learning a Deep Convolutional Network for Image Super-Resolution, Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou, [ECCV], [Project], SRCNN

2015 ICCV

Deep Networks for Image Super-Resolution with Sparse Prior, Wang, Zhaowen; Liu, Ding; Yang, Jianchao; Han, Wei; Huang, Thomas, [arXiv], [Matlab*], [OpenAccess], [Project], SCN

2016 CVPR

Accurate Image Super-Resolution Using Very Deep Convolutional Networks, Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu, [arXiv], [OpenAccess], [Project], VDSR
Deeply-Recursive Convolutional Network for Image Super-Resolution, Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu, [arXiv], [OpenAccess], [Project], DRCN
Seven ways to improve example-based single image super resolution, Timofte, Radu; Rothe, Rasmus; Van Gool, Luc, [arXiv], [OpenAccess], [Project], IA, L20

2016 ECCV

Accelerating the Super-Resolution Convolutional Neural Network, Dong, Chao; Loy, Chen Change; Tang, Xiaoou, [arXiv], [ECCV], [Project], FSRCNN, General-100
Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Johnson, Justin; Alahi, Alexandre; Fei-Fei, Li, [arXiv], [ECCV], [Project], [Torch*], Perceptual loss

2016 ICLR

Super-Resolution with Deep Convolutional Sufficient Statistics, Bruna, Joan; Sprechmann, Pablo; LeCun, Yann, [arXiv], [ICLR]

2016 NIPS

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections, Mao, Xiao-Jiao; Shen, Chunhua; Yang, Yu-Bin, [arXiv], [Caffe*], [NIPS], RED-Net

2016 TCI

Loss Functions for Neural Networks for Image Processing, Zhao, Hang; Gallo, Orazio; Frosio, Iuri; Kautz, Jan, [arXiv], [Caffe*], [Project], [TCI], PL4NN

2016 TPAMI

Image Super-Resolution Using Deep Convolutional Networks, Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou, [arXiv], [Project], [TPAMI], SRCNN

2016 WACV

Is Image Super-resolution Helpful for Other Vision Tasks?, Dai, Dengxin; Wang, Yujian; Chen, Yuhua; Van Gool, Luc, [arXiv], [WACV]

2017 CVPR

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, Lai, Wei-Sheng; Huang, Jia-Bin; Ahuja, Narendra; Yang, Ming-Hsuan, [arXiv], [MatConvNet*], [OpenAccess], [Project], LapSRN
Image Super-Resolution via Deep Recursive Residual Network, Tai, Ying; Yang, Jian; Liu, Xiaoming, [Caffe*], [OpenAccess], [Project], DRRN
Learning Deep CNN Denoiser Prior for Image Restoration, Zhang, Kai; Zuo, Wangmeng; Gu, Shuhang; Zhang, Lei, [arXiv], [MatConvNet*], [OpenAccess], IRCNN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, Ledig, Christian; Theis, Lucas; Huszar, Ferenc; Caballero, Jose; Cunningham, Andrew; Acosta, Alejandro; Aitken, Andrew; Tejani, Alykhan; Totz, Johannes; Wang, Zehan; Shi, Wenzhe, [arXiv], [OpenAccess], SRGAN, SRResNet

2017 CVPRW

A Deep Convolutional Neural Network with Selection Units for Super-Resolution, Choi, Jae-Seok; Kim, Munchurl, [OpenAccess], SelNet
Balanced Two-Stage Residual Networks for Image Super-Resolution, Fan, Yuchen; Shi, Honghui; Yu, Jiahui; Liu, Ding; Han, Wei; Yu, Haichao; Wang, Zhangyang; Wang, Xinchao; Huang, Thomas S., [OpenAccess], BTSRN
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification, Bae, Woong; Yoo, Jaejun; Ye, Jong Chul, [arXiv], [MatConvNet*], [OpenAccess]
Deep Wavelet Prediction for Image Super-Resolution, Guo, Tiantong; Mousavi, Hojjat Seyed; Vu, Tiep Huu; Monga, Vishal, [Matlab*], [OpenAccess], DWSR
Enhanced Deep Residual Networks for Single Image Super-Resolution, Lim, Bee; Son, Sanghyun; Kim, Heewon; Nah, Seungjun; Lee, Kyoung Mu, [arXiv], [OpenAccess], [PyTorch*], [Torch*], EDSR, MDSR
Exploiting Reflectional and Rotational Invariance in Single Image Superresolution, Donn, Simon; Meeus, Laurens; Luong, Hiep Quang; Goossens, Bart; Philips, Wilfried, [OpenAccess], FSRCNN SEF + F
Fast and Accurate Image Super-Resolution Using a Combined Loss, Xu, Jinchang; Zhao, Yu; Dong, Yuan; Bai, Hongliang, [OpenAccess], TLSR
FormResNet: Formatted Residual Learning for Image Restoration, Jiao, Jianbo; Tu, Wei-Chih; He, Shengfeng; Lau, Rynson W. H., [MatConvNet*], [OpenAccess]
Image Super Resolution Based on Fusing Multiple Convolution Neural Networks, Ren, Haoyu; El-Khamy, Mostafa; Lee, Jungwon, [OpenAccess], CNF
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study, Agustsson, Eirikur; Timofte, Radu, [OpenAccess], [Project], NTIRE, DIV2K
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results, Timofte, Radu; Agustsson, Eirikur; Van Gool, Luc; Yang, Ming-Hsuan; Zhang, Lei; Lim, Bee; Son, Sanghyun; Kim, Heewon; Nah, Seungjun; Lee, Kyoung Mu; Wang, Xintao; Tian, Yapeng; Yu, Ke; Zhang, Yulun; Wu, Shixiang; Dong, Chao; Lin, Liang; Qiao, Yu; Loy, Chen Change; Bae, Woong; Yoo, Jaejun; Han, Yoseob; Ye, Jong Chul; Choi, Jae-Seok; Kim, Munchurl; Fan, Yuchen; Yu, Jiahui; Han, Wei; Liu, Ding; Yu, Haichao; Wang, Zhangyang; Shi, Honghui; Wang, Xinchao; Huang, Thomas S; Chen, Yunjin; Zhang, Kai; Zuo, Wangmeng; Tang, Zhimin; Luo, Linkai; Li, Shaohui; Fu, Min; Cao, Lei; Heng, Wen; Bui, Giang; Le, Truc; Duan, Ye; Tao, Dacheng; Wang, Ruxin; Lin, Xu; Pang, Jianxin; Xu, Jinchang; Zhao, Yu; Xu, Xiangyu; Pan, Jinshan; Sun, Deqing; Zhang, Yujin; Song, Xibin; Dai, Yuchao; Qin, Xueying; Huynh, Xuan-Phung; Guo, Tiantong; Mousavi, Hojjat Seyed; Vu, Tiep Huu; Monga, Vishal; Cruz, Cristovao; Egiazarian, Karen; Katkovnik, Vladimir; Mehta, Rakesh; Jain, Arnav Kumar; Agarwalla, Abhinav; Praveen, Ch V Sai; Zhou, Ruofan; Wen, Hongdiao; Zhu, Che; Xia, Zhiqiang; Wang, Zhengtao; Guo, Qi, [OpenAccess], [Project], NTIRE

2017 ICCV

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis, Sajjadi, Mehdi S. M.; Schölkopf, Bernhard; Hirsch, Michael, [arXiv], [OpenAccess], [TensorFlow*], EnhanceNet
Image Super-Resolution Using Dense Skip Connections, Tong, Tong; Li, Gen; Liu, Xiejie; Gao, Qinquan, [OpenAccess], SRDenseNet
MemNet: A Persistent Memory Network for Image Restoration, Tai, Ying; Yang, Jian; Liu, Xiaoming; Xu, Chunyan, [arXiv], [Caffe*], [OpenAccess], MemNet
Pixel Recursive Super Resolution, Dahl, Ryan; Norouzi, Mohammad; Shlens, Jonathon, [arXiv], [OpenAccess]

2017 Pattern Recognition Letters

Convolutional Low-Resolution Fine-Grained Classification, Cai, Dingding; Chen, Ke; Qian, Yanlin; Kämäräinen, Joni-Kristian, [arXiv], [Caffe*], [Pattern Recognition Letters], RACNN

2017 TIP

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei, [arXiv], [Keras/MatConvNet/PyTorch*], [TIP], DnCNN

2018 arXiv

Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution, Hu, Yanting; Li, Jie; Huang, Yuanfei; Gao, Xinbo, [arXiv], CSFM
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality, Choi, Jun-Ho; Kim, Jun-Hyuk; Cheon, Manri; Lee, Jong-Seok, [arXiv], [TensorFlow*], 4PP-EUSR
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss, Guo, Yong; Chen, Qi; Chen, Jian; Huang, Junzhou; Xu, Yanwu; Cao, Jiezhang; Zhao, Peilin; Tan, Mingkui, [arXiv], DRN
Image Reconstruction with Predictive Filter Flow, Kong, Shu; Fowlkes, Charless, [arXiv], [Project], [PyTorch*]
RAM: Residual Attention Module for Single Image Super-Resolution, Kim, Jun-Hyuk; Choi, Jun-Ho; Cheon, Manri; Lee, Jong-Seok, [arXiv], RAM, SRRAM
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network, Kim, Kwanyoung; Chun, Se Young, [arXiv], SREdgeNet
Super-Resolution based on Image-Adapted CNN Denoisers: Incorporating Generalization of Training Data and Internal Learning in Test Time, Tirer, Tom; Giryes, Raja, [arXiv], IDBP
Task-Driven Super Resolution: Object Detection in Low-resolution Images, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, [arXiv], TDSR
Triple Attention Mixed Link Network for Single Image Super Resolution, Cheng, Xi; Li, Xiang; Yang, Jian, [arXiv], TAN
Unsupervised Degradation Learning for Single Image Super-Resolution, Zhao, Tianyu; Ren, Wenqi; Zhang, Changqing; Ren, Dongwei; Hu, Qinghua, [arXiv], DNSR

2018 CVPR

Deep Back-Projection Networks For Super-Resolution, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, [arXiv], [Caffe*], [OpenAccess], [Project], [PyTorch*], DBPN
Fast and Accurate Single Image Super-Resolution via Information Distillation Network, Hui, Zheng; Wang, Xiumei; Gao, Xinbo, [arXiv], [Caffe*], [OpenAccess], [TensorFlow*], IDN
Image Super-Resolution via Dual-State Recurrent Networks, Han, Wei; Chang, Shiyu; Liu, Ding; Yu, Mo; Witbrock, Michael; Huang, Thomas S., [arXiv], [OpenAccess], [TensorFlow*], DSRN
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations, Zhang, Kai; Zuo, Wangmeng; Zhang, Lei, [arXiv], [MatConvNet*], [OpenAccess], [Project], SRMD
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform, Wang, Xintao; Yu, Ke; Dong, Chao; Loy, Chen Change, [arXiv], [OpenAccess], [Project], [PyTorch*], [PyTorch*], SFT-GAN, OutdoorScene
Residual Dense Network for Image Super-Resolution, Zhang, Yulun; Tian, Yapeng; Kong, Yu; Zhong, Bineng; Fu, Yun, [arXiv], [OpenAccess], [PyTorch*], RDN
The Perception-Distortion Tradeoff, Blau, Yochai; Michaeli, Tomer, [arXiv], [OpenAccess], [Project]

2018 CVPRW

A Fully Progressive Approach to Single-Image Super-Resolution, Wang, Yifan; Perazzi, Federico; McWilliams, Brian; Sorkine-Hornung, Alexander; Sorkine-Hornung, Olga; Schroers, Christopher, [arXiv], [OpenAccess], [PyTorch*], ProSR
Deep Residual Network with Enhanced Upscaling Module for Super-Resolution, Kim, Jun-Hyuk; Lee, Jong-Seok, [OpenAccess], [TensorFlow*], EUSR
Efficient Module Based Single Image Super Resolution for Multiple Problems, Park, Dongwon; Kim, Kwanyoung; Chun, Se Young, [OpenAccess], EDSR-PP, EMBSR
Image Super-Resolution via Progressive Cascading Residual Network, Ahn, Namhyuk; Kang, Byungkon; Sohn, Kyung-Ah, [OpenAccess], Progressive CARN
IRGUN : Improved Residue Based Gradual Up-Scaling Network for Single Image Super Resolution, Sharma, Manoj; Mukhopadhyay, Rudrabha; Upadhyay, Avinash; Koundinya, Sriharsha; Shukla, Ankit; Chaudhury, Santanu, [OpenAccess], [TensorFlow*], IRGUN
Large Receptive Field Networks for High-Scale Image Super-Resolution, Seif, George; Androutsos, Dimitrios, [arXiv], [OpenAccess], LRFNet
Multi-level Wavelet-CNN for Image Restoration, Liu, Pengju; Zhang, Hongzhi; Zhang, Kai; Lin, Liang; Zuo, Wangmeng, [arXiv], [MatConvNet*], [OpenAccess], MWCNN
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution, Bei, Yijie; Damian, Alex; Hu, Shijia; Menon, Sachit; Ravi, Nikhil; Rudin, Cynthia, [arXiv], [OpenAccess], [PyTorch*], [TensorFlow*], ADRSR, DNSR
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results, Timofte, Radu; Gu, Shuhang; Wu, Jiqing; Van Gool, Luc, [OpenAccess], NTIRE
Persistent Memory Residual Network for Single Image Super Resolution, Chen, Rong; Qu, Yanyun; Zeng, Kun; Guo, Jinkang; Li, Cuihua; Xie, Yuan, [OpenAccess], MemEDSR, IRMem

2018 DICTA

Deep Bi-Dense Networks for Image Super-Resolution, Wang, Yucheng; Shen, Jialiang; Zhang, Jian, [arXiv], [DICTA], [Torch*], DBDN

2018 ECCV

CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping, Zheng, Haitian; Ji, Mengqi; Wang, Haoqian; Liu, Yebin; Fang, Lu, [arXiv], [OpenAccess], [PyTorch*], CrossNet
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network, Ahn, Namhyuk; Kang, Byungkon; Sohn, Kyung-Ah, [arXiv], [OpenAccess], [PyTorch*], CARN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks, Zhang, Yulun; Li, Kunpeng; Li, Kai; Wang, Lichen; Zhong, Bineng; Fu, Yun, [arXiv], [OpenAccess], [PyTorch*], RCAN
Multi-scale Residual Network for Image Super-Resolution, Li, Juncheng; Fang, Faming; Mei, Kangfu; Zhang, Guixu, [OpenAccess], [PyTorch*], MSRN
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network, Bai, Yancheng; Zhang, Yongqiang; Ding, Mingli; Ghanem, Bernard, [OpenAccess], SOD-MTGAN
SRFeat: Single Image Super-Resolution with Feature Discrimination, Park, Seong-Jin; Son, Hyeongseok; Cho, Sunghyun; Hong, Ki-Sang; Lee, Seungyong, [OpenAccess], [TensorFlow*], SRFeat

2018 ECCVW

Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, Vasu, Subeesh; Madam, Nimisha Thekke; N, Rajagopalan A., [arXiv], [OpenAccess], [PyTorch*], EPSR
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, Wang, Xintao; Yu, Ke; Wu, Shixiang; Gu, Jinjin; Liu, Yihao; Dong, Chao; Loy, Chen Change; Qiao, Yu; Tang, Xiaoou, [arXiv], [OpenAccess], [PyTorch*], [PyTorch*], ESRGAN
Generative adversarial network-based image super-resolution using perceptual content losses, Cheon, Manri; Kim, Jun-Hyuk; Choi, Jun-Ho; Lee, Jong-Seok, [arXiv], [OpenAccess], [TensorFlow*], EUSR-PCL
The 2018 PIRM Challenge on Perceptual Image Super-resolution, Blau, Yochai; Mechrez, Roey; Timofte, Radu; Michaeli, Tomer; Zelnik-Manor, Lihi, [arXiv], [Matlab*], [OpenAccess], [Project1], [Project2], PIRM

2018 NIPS

Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution, Zhong, Zhisheng; Shen, Tiancheng; Yang, Yibo; Lin, Zhouchen; Zhang, Chao, [arXiv], [NIPS], SRCliqueNet
Non-Local Recurrent Network for Image Restoration, Liu, Ding; Wen, Bihan; Fan, Yuchen; Loy, Chen Change; Huang, Thomas S, [arXiv], [NIPS], [TensorFlow*], NLRN

2018 TPAMI

Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks, Lai, Wei-Sheng; Huang, Jia-Bin; Ahuja, Narendra; Yang, Ming-Hsuan, [arXiv], [MatConvNet*], [Project], [TPAMI], MS-LapSRN

2019 arXiv

A Matrix-in-matrix Neural Network for Image Super Resolution, Ma, Hailong; Chu, Xiangxiang; Zhang, Bo; Wan, Shaohua; Zhang, Bo, [arXiv], [PyTorch*], MCAN
Deep Back-Projection Networks for Single Image Super-resolution, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, [arXiv], [Caffe*], [PyTorch*], [Pytorch], DBPN
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search, Chu, Xiangxiang; Zhang, Bo; Ma, Hailong; Xu, Ruijun; Li, Jixiang; Li, Qingyuan, [arXiv], [TensorFlow*], FALSR
Photo-realistic Image Super-resolution with Fast and Lightweight Cascading Residual Network, Ahn, Namhyuk; Kang, Byungkon; Sohn, Kyung-Ah, [arXiv], [PyTorch*], PCARN

2019 CVPR

Blind Super-Resolution With Iterative Kernel Correction, Gu, Jinjin; Lu, Hannan; Zuo, Wangmeng; Dong, Chao, [arXiv], [OpenAccess], IKC
Camera Lens Super-Resolution, Chen, Chang; Xiong, Zhiwei; Tian, Xinmei; Zha, Zheng-Jun; Wu, Feng, [arXiv], [OpenAccess], [TensorFlow*], CameraSR, City100
Deep Network Interpolation for Continuous Imagery Effect Transition, Wang, Xintao; Yu, Ke; Dong, Chao; Tang, Xiaoou; Loy, Chen Change, [arXiv], [OpenAccess], [Project], [PyTorch*], DNI
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels, Zhang, Kai; Zuo, Wangmeng; Zhang, Lei, [arXiv], [OpenAccess], [PyTorch*], DPSR
Feedback Network for Image Super-Resolution, Li, Zhen; Yang, Jinglei; Liu, Zheng; Yang, Xiaomin; Jeon, Gwanggil; Wu, Wei, [arXiv], [OpenAccess], [PyTorch*], SRFBN
Image Super-Resolution by Neural Texture Transfer, Zhang, Zhifei; Wang, Zhaowen; Lin, Zhe; Qi, Hairong, [arXiv], [OpenAccess], [Project], [TensorFlow*], SRNTT, CUFED5
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution, Hu, Xuecai; Mu, Haoyuan; Zhang, Xiangyu; Wang, Zilei; Sun, Jian; Tan, Tieniu, [arXiv], [OpenAccess], [PyTorch*], Meta-SR
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination, Woong Soh, Jae; Yong Park, Gu; Jo, Junho; Ik Cho, Nam, [Code*], [OpenAccess], NatSR
ODE-Inspired Network Design for Single Image Super-Resolution, Xiangyu, He; Zitao, Mo; Peisong, Wang; Yang, Liu; Mingyuan, Yang; Jian, Cheng, [OpenAccess], [PyTorch*], ODE
Residual Networks for Light Field Image Super-Resolution, Zhang, Shuo; Lin, Youfang; Sheng, Hao, [OpenAccess]
Second-order Attention Network for Single Image Super-Resolution, Dai, Tao; Cai, Jianrui; Zhang, Yongbing; Xia, Shu-Tao; Zhang, Lei, [OpenAccess], [PyTorch*], SAN
Towards Real Scene Super-Resolution with Raw Images, Xu, Xiangyu; Ma, Yongrui; Sun, Wenxiu, [arXiv], [OpenAccess], [Project]
Zoom to Learn, Learn to Zoom, Zhang, Xuaner; Chen, Qifeng; Ng, Ren; Koltun, Vladlen, [arXiv], [OpenAccess], [Project1], [Project2], [TensorFlow*], [Video], SR-RAW, CoBi

2019 CVPRW

An Epipolar Volume Autoencoder With Adversarial Loss for Deep Light Field Super-Resolution, Zhu, Minchen; Alperovich, Anna; Johannsen, Ole; Sulc, Antonin; Goldluecke, Bastian, [OpenAccess], DiffWGAN
DenseNet With Deep Residual Channel-Attention Blocks for Single Image Super Resolution, Jang, Dong-Won; Park, Rae-Hong, [OpenAccess], [PyTorch*], DRCA
Encoder-Decoder Residual Network for Real Super-Resolution, Cheng, Guoan; Matsune, Ai; Li, Qiuyu; Zhu, Leilei; Zang, Huaijuan; Zhan, Shu, [OpenAccess], [PyTorch*], EDRN
Fractal Residual Network and Solutions for Real Super-Resolution, Kwak, Junhyung; Son, Donghee, [OpenAccess], FRN
Hierarchical Back Projection Network for Image Super-Resolution, Liu, Zhi-Song; Wang, Li-Wen; Li, Chu-Tak; Siu, Wan-Chi, [OpenAccess], HBPN
Light Field Super-Resolution: A Benchmark, Cheng, Zhen; Xiong, Zhiwei; Chen, Chang; Liu, Dong, [OpenAccess]
Multi-Scale Deep Neural Networks for Real Image Super-Resolution, Gao, Shangqi; Zhuang, Xiahai, [arXiv], [OpenAccess], [TensorFlow*], MsDNN
NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results, Cai, Jianrui; Gu, Shuhang; Timofte, Radu; Zhang, Lei, [OpenAccess], NTIRE
Orientation-Aware Deep Neural Network for Real Image Super-Resolution, Du, Chen; Zewei, He; Anshun, Sun; Jiangxin, Yang; Yanlong, Cao; Yanpeng, Cao; Siliang, Tang; Yang, Michael Ying, [OpenAccess], OA-DNN
SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution, Xu, Xuan; Li, Xin, [OpenAccess], SCAN
Suppressing Model Overfitting for Image Super-Resolution Networks, Feng, Ruicheng; Gu, Jinjin; Qiao, Yu; Dong, Chao, [arXiv], [OpenAccess]

2019 ICLR

RESIDUAL NON-LOCAL ATTENTION NETWORKS FOR IMAGE RESTORATION, Zhang, Yulun; Li, Kunpeng; Li, Kai; Zhong, Bineng; Fu, Yun, [arXiv], [OpenReview], [PyTorch*], RNAN

2019 TPAMI

Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data, Kohler, Thomas; Batz, Michel; Naderi, Farzad; Kaup, Andre; Maier, Andreas; Riess, Christian, [Matlab*], [Project], [TPAMI], SupER

2.2 Face Image SR

2016 ECCV

Deep Cascaded Bi-Network for Face Hallucination, Zhu, Shizhan; Liu, Sifei; Loy, Chen Change; Tang, Xiaoou, [arXiv], [Caffe*], [ECCV], CBN
Ultra-Resolving Face Images by Discriminative Generative Networks, Yu, Xin; Porikli, Fatih, [ECCV], [Torch*], UR-DGN

2017 AAAI

Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks, Yu, Xin; Porikli, Fatih, [AAAI], [Torch*], TDN

2017 CVPR

Attention-Aware Face Hallucination via Deep Reinforcement Learning, Cao, Qingxing; Lin, Liang; Shi, Yukai; Liang, Xiaodan; Li, Guanbin, [arXiv], [OpenAccess], [Torch*], Attention-FH
Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders, Yu, Xin; Porikli, Fatih, [OpenAccess], [Torch*], TDAE

2017 ICCV

Learning to Super-Resolve Blurry Face and Text Images, Xu, Xiangyu; Sun, Deqing; Pan, Jinshan; Zhang, Yujin; Pfister, Hanspeter; Yang, Ming-Hsuan, [OpenAccess], [Project], MCGAN, SCGAN
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution, Huang, Huaibo; He, Ran; Sun, Zhenan; Tan, Tieniu, [OpenAccess], [PyTorch*], Wavelet-SRNet

2017 IJCAI

Learning to Hallucinate Face Images via Component Generation and Enhancement, Song, Yibing; Zhang, Jiawei; He, Shengfeng; Bao, Linchao; Yang, Qingxiong, [arXiv], [IJCAI], [Project], LCGE

2018 CVPR

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors, Chen, Yu; Tai, Ying; Liu, Xiaoming; Shen, Chunhua; Yang, Jian, [arXiv], [OpenAccess], [Torch*], FSRNet
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs, Bulat, Adrian; Tzimiropoulos, Georgios, [arXiv], [OpenAccess], Super-FAN
Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes, Yu, Xin; Fernando, Basura; Hartley, Richard; Porikli, Fatih, [OpenAccess], [Torch*], FaceAttr

2018 CVPRW

Attribute Augmented Convolutional Neural Network for Face Hallucination, Lee, Cheng-Han; Zhang, Kaipeng; Lee, Hu-Cheng; Cheng, Chia-Wen; Hsu, Winston, [OpenAccess], [TensorFlow*], AACNN

2018 ECCV

Face Super-Resolution Guided by Facial Component Heatmaps, Yu, Xin; Fernando, Basura; Ghanem, Bernard; Porikli, Fatih; Hartley, Richard, [OpenAccess], MTUN
Super-Identity Convolutional Neural Network for Face Hallucination, Zhang, Kaipeng; Zhang, Zhanpeng; Cheng, Chia-Wen; Hsu, Winston H.; Qiao, Yu; Liu, Wei; Zhang, Tong, [arXiv], [OpenAccess], SICNN

2019 CVPRW

Exemplar Guided Face Image Super-Resolution Without Facial Landmarks, Dogan, Berk; Gu, Shuhang; Timofte, Radu, [OpenAccess], GWAInet

2.3 Video SR

2015 ICCV

Video Super-Resolution via Deep Draft-Ensemble Learning, Liao, Renjie; Tao, Xin; Li, Ruiyu; Ma, Ziyang; Jia, Jiaya, [OpenAccess], [Project], VideoSR

2015 NIPS

Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution, Huang, Yan; Wang, Wei; Wang, Liang, [NIPS], BRCN

2016 CVPR

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, Shi, Wenzhe; Caballero, Jose; Huszár, Ferenc; Totz, Johannes; Aitken, Andrew P.; Bishop, Rob; Rueckert, Daniel; Wang, Zehan, [arXiv], [OpenAccess], ESPCN, Sub-pixel

2016 ICIP

Super-resolution of compressed videos using convolutional neural networks, Kappeler, Armin; Yoo, Seunghwan; Dai, Qiqin; Katsaggelos, Aggelos K., [ICIP], CVSRnet

2016 TCI

Video Super-Resolution With Convolutional Neural Networks, Kappeler, Armin; Yoo, Seunghwan; Dai, Qiqin; Katsaggelos, Aggelos K., [TCI], VSRNet

2017 AAAI

Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution, Guo, Jun; Chao, Hongyang, [AAAI], STCN

2017 CVPR

Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation, Caballero, Jose; Ledig, Christian; Aitken, Andrew; Acosta, Alejandro; Totz, Johannes; Wang, Zehan; Shi, Wenzhe, [arXiv], [OpenAccess], VESPCN, STN

2017 CVPRW

FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos, Zhang, Zhengdong; Sze, Vivienne, [arXiv], [OpenAccess], [Project], FAST

2017 ICCV

Detail-Revealing Deep Video Super-Resolution, Tao, Xin; Gao, Hongyun; Liao, Renjie; Wang, Jue; Jia, Jiaya, [arXiv], [OpenAccess], [TensorFlow*], SPMC
Robust Video Super-Resolution with Learned Temporal Dynamics, Liu, Ding; Wang, Zhaowen; Fan, Yuchen; Liu, Xianming; Wang, Zhangyang; Chang, Shiyu; Huang, Thomas, [OpenAccess], [Project], Temporal adaptive network

2018 arXiv

Temporally Coherent GANs for Video Super-Resolution (TecoGAN), Chu, Mengyu; Xie, You; Leal-Taixé, Laura; Thuerey, Nils, [arXiv], [PyTorch*], [Video], TecoGAN

2018 CVPR

Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, Jo, Younghyun; Oh, Seoung Wug; Kang, Jaeyeon; Kim, Seon Joo, [OpenAccess], [TensorFlow*], VSR-DUF
Frame-Recurrent Video Super-Resolution, Sajjadi, Mehdi S. M.; Vemulapalli, Raviteja; Brown, Matthew, [arXiv], [OpenAccess], [TensorFlow*], [Video], FRVSR

2018 TIP

Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach, Liu, Ding; Wang, Zhaowen; Fan, Yuchen; Liu, Xianming; Wang, Zhangyang; Chang, Shiyu; Wang, Xinchao; Huang, Thomas S., [Project], [TIP], Temporal adaptive network

2018 TPAMI

Video Super-Resolution via Bidirectional Recurrent Convolutional Networks, Huang, Yan; Wang, Wei; Wang, Liang, [TPAMI], BRCN

2019 CVPR

Fast Spatio-Temporal Residual Network for Video Super-Resolution, Li, Sheng; He, Fengxiang; Du, Bo; Zhang, Lefei; Xu, Yonghao; Tao, Dacheng, [arXiv], [OpenAccess], FSTRN
Recurrent Back-Projection Network for Video Super-Resolution, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, [arXiv], [OpenAccess], [Project], [PyTorch*], RBPN

2019 CVPRW

Adapting Image Super-Resolution State-Of-The-Arts and Learning Multi-Model Ensemble for Video Super-Resolution, Li, Chao; He, Dongliang; Liu, Xiao; Ding, Yukang; Wen, Shilei, [arXiv], [OpenAccess]
An Empirical Investigation of Efficient Spatio-Temporal Modeling in Video Restoration, Fan, Yuchen; Yu, Jiahui; Liu, Ding; Huang, Thomas S, [OpenAccess], [TensorFlow*], WDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks, Wang, Xintao; Chan, Kelvin C. K.; Yu, Ke; Dong, Chao; Loy, Chen Change, [arXiv], [Project], [PyTorch*], EDVR
MultiBoot Vsr: Multi-Stage Multi-Reference Bootstrapping for Video Super-Resolution, Kalarot, Ratheesh; Porikli, Fatih, [OpenAccess], MultiBoot
NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study, Nah, Seungjun; Baik, Sungyong; Hong, Seokil; Moon, Gyeongsik; Son, Sanghyun; Timofte, Radu; Lee, Kyoung Mu, [OpenAccess], NTIRE, REDS
NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results, Nah, Seungjun; Timofte, Radu; Gu, Shuhang; Baik, Sungyong; Hong, Seokil; Moon, Gyeongsik; Son, Sanghyun; Lee, Kyoung Mu, [OpenAccess], NTIRE

2.4 Domain-specific SR

2016 ACCV

Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network, Song, Xibin; Dai, Yuchao; Qin, Xueying, [ACCV]

2016 ECCV

ATGV-Net: Accurate Depth Super-Resolution, Riegler, Gernot; Rüther, Matthias; Bischof, Horst, [arXiv], [ECCV], ATGV-Net
Depth Map Super-Resolution by Deep Multi-Scale Guidance, Hui, Tak-Wai; Loy, Chen Change; Tang, Xiaoou, [ECCV], [Matlab*], [Project], MSG-Net

2017 CVPR

Perceptual Generative Adversarial Networks for Small Object Detection, Li, Jianan; Liang, Xiaodan; Wei, Yunchao; Xu, Tingfa; Feng, Jiashi; Yan, Shuicheng, [arXiv], [OpenAccess], Perceptual GAN

2017 Pattern Recognition

Hyperspectral image reconstruction by deep convolutional neural network for classification, Li, Yunsong; Xie, Weiying; Li, Huaqing, [Pattern Recognition], R-ELM

2018 CVPR

Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior, Jeon, Daniel S.; Baek, Seung-Hwan; Choi, Inchang; Kim, Min H., [OpenAccess], [Project], [TensorFlow*], StereoSR
Feature Super-Resolution: Make Machine See More Clearly, Tan, Weimin; Yan, Bo; Bare, Bahetiyaer, [OpenAccess], FSR
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution, Qu, Ying; Qi, Hairong; Kwan, Chiman, [arXiv], [OpenAccess], [TensorFlow*], uSDN

2018 NIPS

Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation, Smith, Edward; Fujimoto, Scott; Meger, David, [arXiv], [NIPS], [TensorFlow*], MVD

2019 CVPR

3D Appearance Super-Resolution with Deep Learning, Li, Yawei; Tsiminaki, Vagia; Timofte, Radu; Pollefeys, Marc; Van Gool, Luc, [arXiv], [OpenAccess], [PyTorch*], 3DASR
Hyperspectral Image Super-Resolution With Optimized RGB Guidance, Fu, Ying; Zhang, Tao; Zheng, Yinqiang; Zhang, Debing; Huang, Hua, [OpenAccess]
Learning Parallax Attention for Stereo Image Super-Resolution, Wang, Longguang; Wang, Yingqian; Liang, Zhengfa; Lin, Zaiping; Yang, Jungang; An, Wei; Guo, Yulan, [arXiv], [OpenAccess], [PyTorch*], PASSRnet, Flickr1024

2019 TIP

Channel Splitting Network for Single MR Image Super-Resolution, Zhao, Xiaole; Zhang, Yulun; Zhang, Tao; Zou, Xueming, [arXiv], CSN

3. Unsupervised SR

2018 CVPR

"Zero-Shot" Super-Resolution using Deep Internal Learning, Shocher, Assaf; Cohen, Nadav; Irani, Michal, [arXiv], [OpenAccess], [Project], [TensorFlow*], ZSSR
Deep Image Prior, Ulyanov, Dmitry; Vedaldi, Andrea; Lempitsky, Victor, [arXiv], [OpenAccess], [Project], [Python*], Deep image prior

2018 CVPRW

Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks, Yuan, Yuan; Liu, Siyuan; Zhang, Jiawei; Zhang, Yongbing; Dong, Chao; Lin, Liang, [arXiv], [OpenAccess], CinCGAN

2018 ECCV

To learn image super-resolution, use a GAN to learn how to do image degradation first, Bulat, Adrian; Yang, Jing; Tzimiropoulos, Georgios, [arXiv], [OpenAccess], [PyTorch*]

4. SR Datasets

#	Dataset	#Images	#Pixels	Format	Description
1	BSDS300	300 (200/100)	154,401	JPG	Common images, [ICCV], [Project]
2	Set14	14	230,203	PNG	Common images, only 14 images, [Curves and Surfaces]
3	T91	91	58,853	PNG	Common Images, [Project], [TIP]
4	BSDS500	500 (200/100/200)	154,401	JPG	Common images, [Project], [TPAMI]
5	Set5	5	113,491	PNG	Common images, only 5 images, [BMVC], [Project]
6	Urban100	100	774,314	PNG	Images of real-world structures, [Matlab*], [OpenAccess]
7	L20	20	11,577,492	PNG	Common images, very high-resolution, [arXiv], [OpenAccess], [Project]
8	General-100	100	181,108	BMP	Common images with clear edges but fewer smooth regions, [arXiv], [ECCV], [Project]
9	Manga109	109	966,011	PNG	Japanese manga, [MANPU], [Project]
10	DIV2K	1000 (800/100/100)	2,793,250	PNG	Common images, dataset for CVPR competitions (NTIRE), [OpenAccess], [Project]
11	WED	4744	218,664	MAT	Common images, [Project], [TIP]
12	OutdoorScene	10624 (10324/300)	249,593	PNG	Images of outdoor scenes, [arXiv], [OpenAccess], [Project], [PyTorch], [PyTorch]
13	PIRM	200 (100/100)	292,021	PNG	Common images, dataset for ECCV competitions (PIRM), [arXiv], [Matlab*], [OpenAccess], [Project1], [Project2]
14	Flickr1024	2 * 1024 (800/112/112)	734,646	PNG	Stereo images, [arXiv], [Project]
15	3DASR	3 * 24	5,006,868	PNG	3D textures of 3D objects, [arXiv], [OpenAccess], [PyTorch*]
16	City100	100 (95/5)		RAW	Common images characterizing the R-V degradation under DSLR and smartphone cameras, respectively, [arXiv], [OpenAccess], [TensorFlow*]
17	SR-RAW	7 * 500 (400/50/50)		JPG/ARW	Raw images produced by real-world computational zoom, [arXiv], [OpenAccess], [Project], [TensorFlow*]
18	CUFED5	756	174,151	PNG	Reference images, each image group consists of 1 root image and 4 reference images at different similarity level, [arXiv], [OpenAccess], [Project1], [Project2], [TensorFlow*], [Video]

"#Images" represents the total number of images in the dataset, where images generated manually are excluded (e.g., LR images obtained by bicubic down-sampling on a HR image).
"#Pixels" represents the average number of pixels in all the images in the dataset. Since the resolution of the images tends to be different in the dataset, this value can better represent the size of the image in the dataset.
At present, we mainly include image super-resolution datasets, and other datasets (such as face image SR, video SR) will be supplemented later.

Corresponding Papers

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Martin, David; Fowlkes, Charless; Tal, Doron; Malik, Jitendra, ICCV 2001, [ICCV], [Project], BSDS300
On Single Image Scale-Up Using Sparse-Representations, Zeyde, Roman; Elad, Michael; Protter, Matan, Curves and Surfaces 2010, [Curves and Surfaces], Set14
Image Super-Resolution Via Sparse Representation, Yang, Jianchao; John, Wright; Thomas, Huang; Ma, Yi, TIP 2010, [Project], [TIP], T91
Contour Detection and Hierarchical Image Segmentation, Arbeláez, Pablo; Maire, Michael; Fowlkes, Charless; Malik, Jitendra, TPAMI 2011, [Project], [TPAMI], BSDS500
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding, Bevilacqua, Marco; Roumy, Aline; Guillemot, Christine; Morel, Marie-line Alberi, BMVC 2012, [BMVC], [Project], Set5
Single image super-resolution from transformed self-exemplars, Huang, Jia-Bin; Singh, Abhishek; Ahuja, Narendra, CVPR 2015, [Matlab*], [OpenAccess], SelfExSR, Urban100
Seven ways to improve example-based single image super resolution, Timofte, Radu; Rothe, Rasmus; Van Gool, Luc, CVPR 2016, [arXiv], [OpenAccess], [Project], IA, L20
Accelerating the Super-Resolution Convolutional Neural Network, Dong, Chao; Loy, Chen Change; Tang, Xiaoou, ECCV 2016, [arXiv], [ECCV], [Project], FSRCNN, General-100
Manga109 dataset and creation of metadata, Fujimoto, Azuma; Ogawa, Toru; Yamamoto, Kazuyoshi; Matsui, Yusuke; Yamasaki, Toshihiko; Aizawa, Kiyoharu, MANPU 2016, [MANPU], [Project], Manga109
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study, Agustsson, Eirikur; Timofte, Radu, CVPRW 2017, [OpenAccess], [Project], NTIRE, DIV2K
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models, Ma, Kede; Duanmu, Zhengfang; Wu, Qingbo; Wang, Zhou; Yong, Hongwei; Li, Hongliang; Zhang, Lei, TIP 2017, [Project], [TIP], WED
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform, Wang, Xintao; Yu, Ke; Dong, Chao; Loy, Chen Change, CVPR 2018, [arXiv], [OpenAccess], [Project], [PyTorch*], [PyTorch*], SFT-GAN, OutdoorScene
The 2018 PIRM Challenge on Perceptual Image Super-resolution, Blau, Yochai; Mechrez, Roey; Timofte, Radu; Michaeli, Tomer; Zelnik-Manor, Lihi, ECCVW 2018, [arXiv], [Matlab*], [OpenAccess], [Project1], [Project2], PIRM
Flickr1024: A Dataset for Stereo Image Super-Resolution, Wang, Yingqian; Wang, Longguang; Yang, Jungang; An, Wei; Guo, Yulan, arXiv 2019, [arXiv], [Project], Flickr1024
3D Appearance Super-Resolution with Deep Learning, Li, Yawei; Tsiminaki, Vagia; Timofte, Radu; Pollefeys, Marc; Van Gool, Luc, CVPR 2019, [arXiv], [OpenAccess], [PyTorch*], 3DASR
Camera Lens Super-Resolution, Chen, Chang; Xiong, Zhiwei; Tian, Xinmei; Zha, Zheng-Jun; Wu, Feng, CVPR 2019, [arXiv], [OpenAccess], [TensorFlow*], CameraSR, City100
Image Super-Resolution by Neural Texture Transfer, Zhang, Zhifei; Wang, Zhaowen; Lin, Zhe; Qi, Hairong, CVPR 2019, [arXiv], [OpenAccess], [Project], [TensorFlow*], SRNTT, CUFED5
Zoom to Learn, Learn to Zoom, Zhang, Xuaner; Chen, Qifeng; Ng, Ren; Koltun, Vladlen, CVPR 2019, [arXiv], [OpenAccess], [Project1], [Project2], [TensorFlow*], [Video], SR-RAW, CoBi
NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study, Nah, Seungjun; Baik, Sungyong; Hong, Seokil; Moon, Gyeongsik; Son, Sanghyun; Timofte, Radu; Lee, Kyoung Mu, CVPRW 2019, [OpenAccess], NTIRE, REDS

5. SR Metrics

Metric	Papers
MS-SSIM	Multiscale structural similarity for image quality assessment, Wang, Zhou; Simoncelli, Eero P.; Bovik, Alan C., ACSSC 2003, [ACSSC], `MS-SSIM`
SSIM	Image Quality Assessment: From Error Visibility to Structural Similarity, Wang, Zhou; Bovik, Alan C.; Sheikh, Hamid R.; Simoncelli, Eero P, TIP 2004, [TIP], `SSIM`
IFC	An information fidelity criterion for image quality assessment using natural scene statistics, Sheikh, Hamid Rahim; Bovik, Alan Conrad; de Veciana, Gustavo de Veciana, TIP 2005, [TIP], `IFC`
VIF	Image information and visual quality, Sheikh, Hamid Rahim; Bovik, Alan C., TIP 2006, [TIP], `VIF`
FSIM	FSIM: A Feature Similarity Index for Image Quality Assessment, Zhang, Lin; Zhang, Lei; Mou, Xuanqin; Zhang, David, TIP 2011, [Project], [TIP], `FSIM`
NIQE	Making a “Completely Blind” Image Quality Analyzer, Mittal, Anish; Soundararajan, Rajiv; Bovik, Alan C., Signal Processing Letters 2013, [Matlab*], [Signal Processing Letters], `NIQE`
Ma	Learning a no-reference quality metric for single-image super-resolution, Ma, Chao; Yang, Chih-Yuan; Yang, Xiaokang; Yang, Ming-Hsuan, CVIU 2017, [arXiv], [CVIU], [Matlab*], [Project], `Ma`

6. Survey Resources

Taxonomy of our survey

SR Benchmarking (Download the interactive HTML version from here)

ptkin / Awesome-Super-Resolution

Awesome Super-Resolution

1. Non-deep Learning based SR

2. Supervised SR

2.1 Generic Image SR

2.2 Face Image SR

2.3 Video SR

2.4 Domain-specific SR

3. Unsupervised SR

4. SR Datasets

5. SR Metrics

6. Survey Resources

7. Other Resources

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