A curated list of awesome super-resolution resources.
Table of Contents
- Introduction
- Non-deep Learning based SR
- Supervised SR
- 2.1 Generic Image SR
- 2.2 Face Image SR
- 2.3 Video SR
- 2.4 Domain-specific SR
- Unsupervised SR
- SR Datasets
- SR Metrics
- 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}
}
If you have any questions or suggestions, please feel free to contact us.
With the rapid development of deep learning techniques, image super-resolution based on deep learning has received more and more attention. 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.
Description
- Collection criteria (Including but not limited to)
- Published on top CV/AI conference or journal (CVPR, ECCV, ICCV, TIP, TPAMI, etc)
- Very innovative or potentially high-impact
- More concerned with deep learning based works, but also welcome non-deep learning based works
- Each paper contains the following contents
- Paper title
- Authors
- Publication title (conference/journal) and year
- Paper links, project homepage, open source code (official implementation is denoted by *)
- Keywords that may be useful (e.g., name of the proposed method)
- Sorting of papers
- The 1st order: publication year
- The 2nd order: publication title
- The 3rd order: paper title
- Anchored Neighborhood Regression for Fast Example-Based Super-Resolution, Timofte, Radu; De, Vincent; Van Gool, Luc, ICCV 2013, [OpenAccess], [Project],
ANR,GR - Nonparametric Blind Super-resolution, Michaeli, Tomer; Irani, Michal, ICCV 2013, [OpenAccess], [Project],
BlindSR - A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, Timofte, Radu; De Smet, Vincent; Van Gool, Luc, ACCV 2014, [ACCV], [Project],
A+ - A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, Peleg, Tomer; Elad, Michael, TIP 2014, [Matlab*], [TIP]
- Fast and accurate image upscaling with super-resolution forests, Schulter, Samuel; Leistner, Christian; Bischof, Horst, CVPR 2015, [OpenAccess]
- Fast and accurate image upscaling with super-resolution forests, Schulter, Samuel; Leistner, Christian; Bischof, Horst, CVPR 2015, [OpenAccess],
RFL - Handling motion blur in multi-frame super-resolution, Ma, Ziyang; Liao, Renjie; Tao, Xin; Xu, Li; Jia, Jiaya; Wu, Enhua, CVPR 2015, [OpenAccess], [Project]
- Single image super-resolution from transformed self-exemplars, Huang, Jia-Bin; Singh, Abhishek; Ahuja, Narendra, CVPR 2015, [Matlab*], [OpenAccess],
SelfExSR,Urban100 - Naive Bayes Super-Resolution Forest, Salvador, Jordi; Perez-Pellitero, Eduardo, ICCV 2015, [OpenAccess], [Project],
NBSRF - PSyCo: Manifold Span Reduction for Super Resolution, Perez-Pellitero, Eduardo; Salvador, Jordi; Ruiz-Hidalgo, Javier; Rosenhahn, Bodo, CVPR 2016, [C++/Matlab*], [OpenAccess],
PSyCo - Learning Parametric Sparse Models for Image Super-Resolution, Li, Yongbo; Dong, Weisheng; Xie, Xuemei; Shi, GUANGMING; Li, Xin; Xu, Donglai, NIPS 2016, [NIPS]
- 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 - 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., CVPR 2017, [arXiv], [OpenAccess],
WEENIE - SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests, Huang, Jun-Jie; Liu, Tianrui; Dragotti, Pier Luigi; Stathaki, Tania, CVPRW 2017, [OpenAccess],
SRHRF+ - Amortised MAP Inference for Image Super-resolution, Sønderby, Casper Kaae; Caballero, Jose; Theis, Lucas; Shi, Wenzhe; Huszár, Ferenc, ICLR 2017, [arXiv], [OpenReview],
AffGAN - RAISR: Rapid and Accurate Image Super Resolution, Romano, Yaniv; Isidoro, John; Milanfar, Peyman, TCI 2017, [arXiv], [TCI],
RAISR - Fight Ill-Posedness with Ill-Posedness: Single-shot Variational Depth Super-Resolution from Shading, Haefner, Bjoern; Queau, Yvain; Mollenhoff, Thomas; Cremers, Daniel, CVPR 2018, [Matlab*], [OpenAccess]
- Hallucinating Compressed Face Images, Yang, Chih-Yuan; Liu, Sifei; Yang, Ming-Hsuan, IJCV 2018, [IJCV], [Matlab*], [Project],
FHCI
- Learning a Deep Convolutional Network for Image Super-Resolution, Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou, ECCV 2014, [ECCV], [Project],
SRCNN - Deep Networks for Image Super-Resolution with Sparse Prior, Wang, Zhaowen; Liu, Ding; Yang, Jianchao; Han, Wei; Huang, Thomas, ICCV 2015, [arXiv], [Matlab*], [OpenAccess], [Project],
SCN - Accurate Image Super-Resolution Using Very Deep Convolutional Networks, Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu, CVPR 2016, [arXiv], [OpenAccess], [Project],
VDSR - Deeply-Recursive Convolutional Network for Image Super-Resolution, Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu, CVPR 2016, [arXiv], [OpenAccess], [Project],
DRCN - 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 - Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Johnson, Justin; Alahi, Alexandre; Fei-Fei, Li, ECCV 2016, [arXiv], [ECCV], [Project], [Torch*],
Perceptual loss - Super-Resolution with Deep Convolutional Sufficient Statistics, Bruna, Joan; Sprechmann, Pablo; LeCun, Yann, ICLR 2016, [arXiv], [ICLR]
- Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections, Mao, Xiao-Jiao; Shen, Chunhua; Yang, Yu-Bin, NIPS 2016, [arXiv], [Caffe*], [NIPS],
RED-Net - Loss Functions for Neural Networks for Image Processing, Zhao, Hang; Gallo, Orazio; Frosio, Iuri; Kautz, Jan, TCI 2016, [arXiv], [Caffe*], [Project], [TCI],
PL4NN - Image Super-Resolution Using Deep Convolutional Networks, Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou, TPAMI 2016, [arXiv], [Project], [TPAMI],
SRCNN - Is Image Super-resolution Helpful for Other Vision Tasks?, Dai, Dengxin; Wang, Yujian; Chen, Yuhua; Van Gool, Luc, WACV 2016, [arXiv], [WACV]
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, Lai, Wei-Sheng; Huang, Jia-Bin; Ahuja, Narendra; Yang, Ming-Hsuan, CVPR 2017, [arXiv], [MatConvNet*], [OpenAccess], [Project],
LapSRN - Image Super-Resolution via Deep Recursive Residual Network, Tai, Ying; Yang, Jian; Liu, Xiaoming, CVPR 2017, [Caffe*], [OpenAccess], [Project],
DRRN - Learning Deep CNN Denoiser Prior for Image Restoration, Zhang, Kai; Zuo, Wangmeng; Gu, Shuhang; Zhang, Lei, CVPR 2017, [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, CVPR 2017, [arXiv], [OpenAccess],
SRGAN,SRResNet - A Deep Convolutional Neural Network with Selection Units for Super-Resolution, Choi, Jae-Seok; Kim, Munchurl, CVPRW 2017, [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., CVPRW 2017, [OpenAccess],
BTSRN - Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification, Bae, Woong; Yoo, Jaejun; Ye, Jong Chul, CVPRW 2017, [arXiv], [MatConvNet*], [OpenAccess]
- Deep Wavelet Prediction for Image Super-Resolution, Guo, Tiantong; Mousavi, Hojjat Seyed; Vu, Tiep Huu; Monga, Vishal, CVPRW 2017, [Matlab*], [OpenAccess],
DWSR - Enhanced Deep Residual Networks for Single Image Super-Resolution, Lim, Bee; Son, Sanghyun; Kim, Heewon; Nah, Seungjun; Lee, Kyoung Mu, CVPRW 2017, [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, CVPRW 2017, [OpenAccess],
FSRCNN SEF + F - Fast and Accurate Image Super-Resolution Using a Combined Loss, Xu, Jinchang; Zhao, Yu; Dong, Yuan; Bai, Hongliang, CVPRW 2017, [OpenAccess],
TLSR - FormResNet: Formatted Residual Learning for Image Restoration, Jiao, Jianbo; Tu, Wei-Chih; He, Shengfeng; Lau, Rynson W. H., CVPRW 2017, [MatConvNet*], [OpenAccess]
- Image Super Resolution Based on Fusing Multiple Convolution Neural Networks, Ren, Haoyu; El-Khamy, Mostafa; Lee, Jungwon, CVPRW 2017, [OpenAccess],
CNF - NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study, Agustsson, Eirikur; Timofte, Radu, CVPRW 2017, [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, CVPRW 2017, [OpenAccess], [Project],
NTIRE - EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis, Sajjadi, Mehdi S. M.; Schölkopf, Bernhard; Hirsch, Michael, ICCV 2017, [arXiv], [OpenAccess], [TensorFlow*],
EnhanceNet - Image Super-Resolution Using Dense Skip Connections, Tong, Tong; Li, Gen; Liu, Xiejie; Gao, Qinquan, ICCV 2017, [OpenAccess],
SRDenseNet - MemNet: A Persistent Memory Network for Image Restoration, Tai, Ying; Yang, Jian; Liu, Xiaoming; Xu, Chunyan, ICCV 2017, [arXiv], [Caffe*], [OpenAccess],
MemNet - Pixel Recursive Super Resolution, Dahl, Ryan; Norouzi, Mohammad; Shlens, Jonathon, ICCV 2017, [arXiv], [OpenAccess]
- Convolutional Low-Resolution Fine-Grained Classification, Cai, Dingding; Chen, Ke; Qian, Yanlin; Kämäräinen, Joni-Kristian, Pattern Recognition Letters 2017, [arXiv], [Caffe*], [Pattern Recognition Letters],
RACNN - Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei, TIP 2017, [arXiv], [Keras/MatConvNet/PyTorch*], [TIP],
DnCNN - Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution, Hu, Yanting; Li, Jie; Huang, Yuanfei; Gao, Xinbo, arXiv 2018, [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 2018, [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 2018, [arXiv],
DRN - Image Reconstruction with Predictive Filter Flow, Kong, Shu; Fowlkes, Charless, arXiv 2018, [arXiv], [Project], [PyTorch*]
- RAM: Residual Attention Module for Single Image Super-Resolution, Kim, Jun-Hyuk; Choi, Jun-Ho; Cheon, Manri; Lee, Jong-Seok, arXiv 2018, [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 2018, [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 2018, [arXiv],
IDBP - Task-Driven Super Resolution: Object Detection in Low-resolution Images, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, arXiv 2018, [arXiv],
TDSR - Triple Attention Mixed Link Network for Single Image Super Resolution, Cheng, Xi; Li, Xiang; Yang, Jian, arXiv 2018, [arXiv],
TAN - Unsupervised Degradation Learning for Single Image Super-Resolution, Zhao, Tianyu; Ren, Wenqi; Zhang, Changqing; Ren, Dongwei; Hu, Qinghua, arXiv 2018, [arXiv],
DNSR - Deep Back-Projection Networks For Super-Resolution, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, CVPR 2018, [arXiv], [Caffe*], [OpenAccess], [Project], [PyTorch*],
DBPN - Fast and Accurate Single Image Super-Resolution via Information Distillation Network, Hui, Zheng; Wang, Xiumei; Gao, Xinbo, CVPR 2018, [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., CVPR 2018, [arXiv], [OpenAccess], [TensorFlow*],
DSRN - Learning a Single Convolutional Super-Resolution Network for Multiple Degradations, Zhang, Kai; Zuo, Wangmeng; Zhang, Lei, CVPR 2018, [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, CVPR 2018, [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, CVPR 2018, [arXiv], [OpenAccess], [PyTorch*],
RDN - The Perception-Distortion Tradeoff, Blau, Yochai; Michaeli, Tomer, CVPR 2018, [arXiv], [OpenAccess], [Project]
- A Fully Progressive Approach to Single-Image Super-Resolution, Wang, Yifan; Perazzi, Federico; McWilliams, Brian; Sorkine-Hornung, Alexander; Sorkine-Hornung, Olga; Schroers, Christopher, CVPRW 2018, [arXiv], [OpenAccess], [PyTorch*],
ProSR - Deep Residual Network with Enhanced Upscaling Module for Super-Resolution, Kim, Jun-Hyuk; Lee, Jong-Seok, CVPRW 2018, [OpenAccess], [TensorFlow*],
EUSR - Efficient Module Based Single Image Super Resolution for Multiple Problems, Park, Dongwon; Kim, Kwanyoung; Chun, Se Young, CVPRW 2018, [OpenAccess],
EDSR-PP,EMBSR - Image Super-Resolution via Progressive Cascading Residual Network, Ahn, Namhyuk; Kang, Byungkon; Sohn, Kyung-Ah, CVPRW 2018, [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, CVPRW 2018, [OpenAccess], [TensorFlow*],
IRGUN - Large Receptive Field Networks for High-Scale Image Super-Resolution, Seif, George; Androutsos, Dimitrios, CVPRW 2018, [arXiv], [OpenAccess],
LRFNet - Multi-level Wavelet-CNN for Image Restoration, Liu, Pengju; Zhang, Hongzhi; Zhang, Kai; Lin, Liang; Zuo, Wangmeng, CVPRW 2018, [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, CVPRW 2018, [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, CVPRW 2018, [OpenAccess],
NTIRE - Persistent Memory Residual Network for Single Image Super Resolution, Chen, Rong; Qu, Yanyun; Zeng, Kun; Guo, Jinkang; Li, Cuihua; Xie, Yuan, CVPRW 2018, [OpenAccess],
MemEDSR,IRMem - Deep Bi-Dense Networks for Image Super-Resolution, Wang, Yucheng; Shen, Jialiang; Zhang, Jian, DICTA 2018, [arXiv], [DICTA], [Torch*],
DBDN - CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping, Zheng, Haitian; Ji, Mengqi; Wang, Haoqian; Liu, Yebin; Fang, Lu, ECCV 2018, [arXiv], [OpenAccess], [PyTorch*],
CrossNet - Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network, Ahn, Namhyuk; Kang, Byungkon; Sohn, Kyung-Ah, ECCV 2018, [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, ECCV 2018, [arXiv], [OpenAccess], [PyTorch*],
RCAN - Multi-scale Residual Network for Image Super-Resolution, Li, Juncheng; Fang, Faming; Mei, Kangfu; Zhang, Guixu, ECCV 2018, [OpenAccess], [PyTorch*],
MSRN - SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network, Bai, Yancheng; Zhang, Yongqiang; Ding, Mingli; Ghanem, Bernard, ECCV 2018, [OpenAccess],
SOD-MTGAN - SRFeat: Single Image Super-Resolution with Feature Discrimination, Park, Seong-Jin; Son, Hyeongseok; Cho, Sunghyun; Hong, Ki-Sang; Lee, Seungyong, ECCV 2018, [OpenAccess], [TensorFlow*],
SRFeat - Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, Vasu, Subeesh; Madam, Nimisha Thekke; N, Rajagopalan A., ECCVW 2018, [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, ECCVW 2018, [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, ECCVW 2018, [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, ECCVW 2018, [arXiv], [Matlab*], [OpenAccess], [Project1], [Project2],
PIRM - Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution, Zhong, Zhisheng; Shen, Tiancheng; Yang, Yibo; Lin, Zhouchen; Zhang, Chao, NIPS 2018, [arXiv], [NIPS],
SRCliqueNet - Non-Local Recurrent Network for Image Restoration, Liu, Ding; Wen, Bihan; Fan, Yuchen; Loy, Chen Change; Huang, Thomas S, NIPS 2018, [arXiv], [NIPS], [TensorFlow*],
NLRN - Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks, Lai, Wei-Sheng; Huang, Jia-Bin; Ahuja, Narendra; Yang, Ming-Hsuan, TPAMI 2018, [arXiv], [MatConvNet*], [Project], [TPAMI],
MS-LapSRN - A Matrix-in-matrix Neural Network for Image Super Resolution, Ma, Hailong; Chu, Xiangxiang; Zhang, Bo; Wan, Shaohua; Zhang, Bo, arXiv 2019, [arXiv], [PyTorch*],
MCAN - Deep Back-Projection Networks for Single Image Super-resolution, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, arXiv 2019, [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 2019, [arXiv], [TensorFlow*],
FALSR - Photo-realistic Image Super-resolution with Fast and Lightweight Cascading Residual Network, Ahn, Namhyuk; Kang, Byungkon; Sohn, Kyung-Ah, arXiv 2019, [arXiv], [PyTorch*],
PCARN - Blind Super-Resolution With Iterative Kernel Correction, Gu, Jinjin; Lu, Hannan; Zuo, Wangmeng; Dong, Chao, CVPR 2019, [arXiv], [OpenAccess],
IKC - Camera Lens Super-Resolution, Chen, Chang; Xiong, Zhiwei; Tian, Xinmei; Zha, Zheng-Jun; Wu, Feng, CVPR 2019, [arXiv], [OpenAccess], [TensorFlow*],
CameraSR,City100 - Deep Network Interpolation for Continuous Imagery Effect Transition, Wang, Xintao; Yu, Ke; Dong, Chao; Tang, Xiaoou; Loy, Chen Change, CVPR 2019, [arXiv], [OpenAccess], [Project], [PyTorch*],
DNI - Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels, Zhang, Kai; Zuo, Wangmeng; Zhang, Lei, CVPR 2019, [arXiv], [OpenAccess], [PyTorch*],
DPSR - Feedback Network for Image Super-Resolution, Li, Zhen; Yang, Jinglei; Liu, Zheng; Yang, Xiaomin; Jeon, Gwanggil; Wu, Wei, CVPR 2019, [arXiv], [OpenAccess], [PyTorch*],
SRFBN - Image Super-Resolution by Neural Texture Transfer, Zhang, Zhifei; Wang, Zhaowen; Lin, Zhe; Qi, Hairong, CVPR 2019, [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, CVPR 2019, [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, CVPR 2019, [Code*], [OpenAccess],
NatSR - ODE-Inspired Network Design for Single Image Super-Resolution, Xiangyu, He; Zitao, Mo; Peisong, Wang; Yang, Liu; Mingyuan, Yang; Jian, Cheng, CVPR 2019, [OpenAccess], [PyTorch*],
ODE - Residual Networks for Light Field Image Super-Resolution, Zhang, Shuo; Lin, Youfang; Sheng, Hao, CVPR 2019, [OpenAccess]
- Second-order Attention Network for Single Image Super-Resolution, Dai, Tao; Cai, Jianrui; Zhang, Yongbing; Xia, Shu-Tao; Zhang, Lei, CVPR 2019, [OpenAccess], [PyTorch*],
SAN - Towards Real Scene Super-Resolution with Raw Images, Xu, Xiangyu; Ma, Yongrui; Sun, Wenxiu, CVPR 2019, [arXiv], [OpenAccess], [Project]
- 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 - RESIDUAL NON-LOCAL ATTENTION NETWORKS FOR IMAGE RESTORATION, Zhang, Yulun; Li, Kunpeng; Li, Kai; Zhong, Bineng; Fu, Yun, ICLR 2019, [arXiv], [OpenReview], [PyTorch*],
RNAN - 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, TPAMI 2019, [Matlab*], [Project], [TPAMI],
SupER
- Deep Cascaded Bi-Network for Face Hallucination, Zhu, Shizhan; Liu, Sifei; Loy, Chen Change; Tang, Xiaoou, ECCV 2016, [arXiv], [Caffe*], [ECCV],
CBN - Ultra-Resolving Face Images by Discriminative Generative Networks, Yu, Xin; Porikli, Fatih, ECCV 2016, [ECCV], [Torch*],
UR-DGN - Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks, Yu, Xin; Porikli, Fatih, AAAI 2017, [AAAI], [Torch*],
TDN - Attention-Aware Face Hallucination via Deep Reinforcement Learning, Cao, Qingxing; Lin, Liang; Shi, Yukai; Liang, Xiaodan; Li, Guanbin, CVPR 2017, [arXiv], [OpenAccess], [Torch*],
Attention-FH - Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders, Yu, Xin; Porikli, Fatih, CVPR 2017, [OpenAccess], [Torch*],
TDAE - Learning to Super-Resolve Blurry Face and Text Images, Xu, Xiangyu; Sun, Deqing; Pan, Jinshan; Zhang, Yujin; Pfister, Hanspeter; Yang, Ming-Hsuan, ICCV 2017, [OpenAccess], [Project],
MCGAN,SCGAN - Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution, Huang, Huaibo; He, Ran; Sun, Zhenan; Tan, Tieniu, ICCV 2017, [OpenAccess], [PyTorch*],
Wavelet-SRNet - Learning to Hallucinate Face Images via Component Generation and Enhancement, Song, Yibing; Zhang, Jiawei; He, Shengfeng; Bao, Linchao; Yang, Qingxiong, IJCAI 2017, [arXiv], [IJCAI], [Project],
LCGE - FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors, Chen, Yu; Tai, Ying; Liu, Xiaoming; Shen, Chunhua; Yang, Jian, CVPR 2018, [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, CVPR 2018, [arXiv], [OpenAccess],
Super-FAN - Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes, Yu, Xin; Fernando, Basura; Hartley, Richard; Porikli, Fatih, CVPR 2018, [OpenAccess], [Torch*],
FaceAttr - Attribute Augmented Convolutional Neural Network for Face Hallucination, Lee, Cheng-Han; Zhang, Kaipeng; Lee, Hu-Cheng; Cheng, Chia-Wen; Hsu, Winston, CVPRW 2018, [OpenAccess], [TensorFlow*],
AACNN - Face Super-Resolution Guided by Facial Component Heatmaps, Yu, Xin; Fernando, Basura; Ghanem, Bernard; Porikli, Fatih; Hartley, Richard, ECCV 2018, [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, ECCV 2018, [arXiv], [OpenAccess],
SICNN
- Video Super-Resolution via Deep Draft-Ensemble Learning, Liao, Renjie; Tao, Xin; Li, Ruiyu; Ma, Ziyang; Jia, Jiaya, ICCV 2015, [OpenAccess], [Project],
VideoSR - Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution, Huang, Yan; Wang, Wei; Wang, Liang, NIPS 2015, [NIPS],
BRCN - 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, CVPR 2016, [arXiv], [OpenAccess],
ESPCN,Sub-pixel - Super-resolution of compressed videos using convolutional neural networks, Kappeler, Armin; Yoo, Seunghwan; Dai, Qiqin; Katsaggelos, Aggelos K., ICIP 2016, [ICIP],
CVSRnet - Video Super-Resolution With Convolutional Neural Networks, Kappeler, Armin; Yoo, Seunghwan; Dai, Qiqin; Katsaggelos, Aggelos K., TCI 2016, [TCI],
VSRNet - Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution, Guo, Jun; Chao, Hongyang, AAAI 2017, [AAAI],
STCN - 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, CVPR 2017, [arXiv], [OpenAccess],
VESPCN, STN - FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos, Zhang, Zhengdong; Sze, Vivienne, CVPRW 2017, [arXiv], [OpenAccess], [Project],
FAST - Detail-Revealing Deep Video Super-Resolution, Tao, Xin; Gao, Hongyun; Liao, Renjie; Wang, Jue; Jia, Jiaya, ICCV 2017, [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, ICCV 2017, [OpenAccess], [Project],
Temporal adaptive network - Temporally Coherent GANs for Video Super-Resolution (TecoGAN), Chu, Mengyu; Xie, You; Leal-Taixé, Laura; Thuerey, Nils, arXiv 2018, [arXiv], [PyTorch*], [Video],
TecoGAN - Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, Jo, Younghyun; Oh, Seoung Wug; Kang, Jaeyeon; Kim, Seon Joo, CVPR 2018, [OpenAccess], [TensorFlow*],
VSR-DUF - Frame-Recurrent Video Super-Resolution, Sajjadi, Mehdi S. M.; Vemulapalli, Raviteja; Brown, Matthew, CVPR 2018, [arXiv], [OpenAccess], [TensorFlow*], [Video],
FRVSR - 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., TIP 2018, [Project], [TIP],
Temporal adaptive network - Video Super-Resolution via Bidirectional Recurrent Convolutional Networks, Huang, Yan; Wang, Wei; Wang, Liang, TPAMI 2018, [TPAMI],
BRCN - Fast Spatio-Temporal Residual Network for Video Super-Resolution, Li, Sheng; He, Fengxiang; Du, Bo; Zhang, Lefei; Xu, Yonghao; Tao, Dacheng, CVPR 2019, [arXiv], [OpenAccess],
FSTRN - Recurrent Back-Projection Network for Video Super-Resolution, Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi, CVPR 2019, [arXiv], [OpenAccess], [Project], [PyTorch*],
RBPN - EDVR: Video Restoration with Enhanced Deformable Convolutional Networks, Wang, Xintao; Chan, Kelvin C. K.; Yu, Ke; Dong, Chao; Loy, Chen Change, CVPRW 2019, [arXiv], [Project], [PyTorch*],
EDVR
- Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network, Song, Xibin; Dai, Yuchao; Qin, Xueying, ACCV 2016, [ACCV]
- ATGV-Net: Accurate Depth Super-Resolution, Riegler, Gernot; Rüther, Matthias; Bischof, Horst, ECCV 2016, [arXiv], [ECCV],
ATGV-Net - Depth Map Super-Resolution by Deep Multi-Scale Guidance, Hui, Tak-Wai; Loy, Chen Change; Tang, Xiaoou, ECCV 2016, [ECCV], [Matlab*], [Project],
MSG-Net - Perceptual Generative Adversarial Networks for Small Object Detection, Li, Jianan; Liang, Xiaodan; Wei, Yunchao; Xu, Tingfa; Feng, Jiashi; Yan, Shuicheng, CVPR 2017, [arXiv], [OpenAccess],
Perceptual GAN - Hyperspectral image reconstruction by deep convolutional neural network for classification, Li, Yunsong; Xie, Weiying; Li, Huaqing, Pattern Recognition 2017, [Pattern Recognition],
R-ELM - Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior, Jeon, Daniel S.; Baek, Seung-Hwan; Choi, Inchang; Kim, Min H., CVPR 2018, [OpenAccess], [Project], [TensorFlow*],
StereoSR - Feature Super-Resolution: Make Machine See More Clearly, Tan, Weimin; Yan, Bo; Bare, Bahetiyaer, CVPR 2018, [OpenAccess],
FSR - Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution, Qu, Ying; Qi, Hairong; Kwan, Chiman, CVPR 2018, [arXiv], [OpenAccess], [TensorFlow*],
uSDN - Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation, Smith, Edward; Fujimoto, Scott; Meger, David, NIPS 2018, [arXiv], [NIPS], [TensorFlow*],
MVD - 3D Appearance Super-Resolution with Deep Learning, Li, Yawei; Tsiminaki, Vagia; Timofte, Radu; Pollefeys, Marc; Van Gool, Luc, CVPR 2019, [arXiv], [OpenAccess], [PyTorch*],
3DASR - Hyperspectral Image Super-Resolution With Optimized RGB Guidance, Fu, Ying; Zhang, Tao; Zheng, Yinqiang; Zhang, Debing; Huang, Hua, CVPR 2019, [OpenAccess]
- Learning Parallax Attention for Stereo Image Super-Resolution, Wang, Longguang; Wang, Yingqian; Liang, Zhengfa; Lin, Zaiping; Yang, Jungang; An, Wei; Guo, Yulan, CVPR 2019, [arXiv], [OpenAccess], [PyTorch*],
PASSRnet,Flickr1024 - Channel Splitting Network for Single MR Image Super-Resolution, Zhao, Xiaole; Zhang, Yulun; Zhang, Tao; Zou, Xueming, TIP 2019, [arXiv],
CSN
- "Zero-Shot" Super-Resolution using Deep Internal Learning, Shocher, Assaf; Cohen, Nadav; Irani, Michal, CVPR 2018, [arXiv], [OpenAccess], [Project], [TensorFlow*],
ZSSR - Deep Image Prior, Ulyanov, Dmitry; Vedaldi, Andrea; Lempitsky, Victor, CVPR 2018, [arXiv], [OpenAccess], [Project], [Python*],
Deep image prior - Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks, Yuan, Yuan; Liu, Siyuan; Zhang, Jiawei; Zhang, Yongbing; Dong, Chao; Lin, Liang, CVPRW 2018, [arXiv], [OpenAccess],
CinCGAN - To learn image super-resolution, use a GAN to learn how to do image degradation first, Bulat, Adrian; Yang, Jing; Tzimiropoulos, Georgios, ECCV 2018, [arXiv], [OpenAccess], [PyTorch*]
| # | 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
| 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 |
- Papers With Code : Super Resolution
- YapengTian/Single-Image-Super-Resolution
- LoSealL/VideoSuperResolution
