By Yapeng Tian and Yunlun Zhang (if you have any suggestions, please contact us! Email: email@example.com OR firstname.lastname@example.org).
Tip: For SR beginners, I recommend you to read some early learning based SISR works which will help understand the problem.
Early learning-based methods
 Freeman, William T and Pasztor, Egon C and Carmichael, Owen T, Learning low-level vision, IJCV, 2000. [Paper] (Freeman et al. first presented example-based or learning-based super-resolution framework - learn relationships between low-resolution image patches and its high-resolution counterparts.)
 Freeman, William T and Jones, Thouis R and Pasztor, Egon C, Example-based super-resolution, IEEE Computer graphics and Applications, 2002. [Paper]
 Chang, Hong and Yeung, Dit-Yan and Xiong, Yimin, Super-resolution through neighbor embedding, CVPR, 2004. [Paper] [Code] (The idea that low-resolution patches and corresponding high-resolution patches share similar local geometries highly influences the subsequent coding-based or dictionary-based methods.)
 Yang, Jianchao and Wright, John and Huang, Thomas S and Ma, Yi, Image super-resolution via sparse representation, IEEE trans. image processing 2010. [paper] [Code] (SCSR: Classical sparsity-based SISR method - use sparse coding technique to learn low-resolution and high-resolution dictionaries.)
 Zeyde, Roman and Elad, Michael and Protter, Matan, On single image scale-up using sparse-representations, International conference on curves and surfaces, 2010. [Paper] [Code] (Low dimension feature speeds up the algorithm. Many sparsity-based image restoration techniques can be found in Prof. Elad’s Website!)
 Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization, TIP, 2011. [Website] (Clustering is a very effective trick and local and nonlocal regularization terms are very powerful! Other good sparsity-based super-resolution methods can be found in Prof. Lei Zhang’s and Weisheng Dong’s Website!)
 Peleg, Tomer and Elad, Michael, A statistical prediction model based on sparse representations for single image super-resolution, TIP, 2014. [Paper] [Code] (Predict the relationships between Low-resolution and high-resolution representation coefficients.)
Super-resolution via self-examplars
 Daniel Glasner, Shai Bagon and Michal, Irani, Super-Resolution from a Single Image, ICCV, 2009. [Paper]
 Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, “Single Image Super-Resolution from Transformed Self-Exemplars”, CVPR, 2015. [Project].
Locally Linear Regression
 Gu, Shuhang and Sang, Nong and Ma, Fan, Fast Image Super Resolution via Local Regression, ICPR, 2012. [Paper] (Kmeans clusetering + ridge regression)
 Timofte, Radu and Rothe, Rasmus and Van Gool, Luc, Seven Ways to Improve Example-Based Single Image Super Resolution, CVPR, 2016. [Website]
 Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a deep convolutional network for image super-resolution, ECCV, 2014. [Website] (first introduce CNN to solve single image super-resolution.)
 Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016. [Website] (use more training data and achieve better SR performance.) [Keras]
 Wang, Zhaowen and Liu, Ding and Yang, Jianchao and Han, Wei and Huang, Thomas, Deep networks for image super-resolution with sparse prior, ICCV, 2015. [Website]
 Shi, Wenzhe and Caballero, Jose and Huszar, Ferenc and Totz, Johannes and Aitken, Andrew P. and Bishop, Rob and Rueckert, Daniel and Wang, Zehan, Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, CVPR, 2016. [Paper]
 Justin Johnson, Alexandre Alahi, Fei-Fei Li, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, ECCV, 2016, [Website] (Perceptual Loss)
 Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, arXiv, 2016. [Paper] (Perceptual Loss, Great Performance!)
 Julien Maira, End-to-End Kernel Learning with Supervised Convolutional Kernel Networks, NIPS, 2016. [Paper]
 Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang, Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections, arXiv, 2016. [Paper]
 Joan Bruna, Pablo Sprechmann, Yann LeCun, SUPER-RESOLUTION WITH DEEP CONVOLUTIONAL SUFFICIENT STATISTICS, ICLR, 2016. [Paper] (Perceptual Loss)
 Mehdi S. M. Sajjadi, Bernhard Schölkopf, Michael Hirsch, EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis, ICCV, 2017. [Paper] (adversarial training + Texture matching loss to reduce unnatural textures produced by perceptual loss)
 Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár, Amortised MAP Inference for Image Super-resolution, ICLR, 2017. [Paper] (calculate the MAP estimate directly using a convolutional neural network)
 Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang, Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, CVPR, 2017. [Website]
 K. Zhang, W. Zuo, S. Gu and L. Zhang, “Learning Deep CNN Denoiser Prior for Image Restoration,” CVPR, 2017. [Code]
 Ying Tai, Jian Yang, and Xiaoming Liu. Image Super-Resolution via Deep Recursive Residual Network, CVPR, 2017. [Code]
 E. Agustsson, R. Timofte, L. Van Gool. Anchored Regression Networks applied to Age Estimation and Super Resolution, ICCV, 2017. [paper]
 Ying Tai, Jian Yang, Xiaoming Liu and Chunyan Xu. MemNet: A Persistent Memory Network for Image Restoration, ICCV, 2017. [code]
 Radu Timofte et al. NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results, CVPRW, 2017. [Paper]
 Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao. Image Super-Resolution Using Dense Skip Connections. ICCV, 2017. [Paper]
 Adrian Bulat and Georgios Tzimiropoulos. Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs. CVPR 2018. [Paper]
 Bjoern Haefner, Yvain Queau, Thomas Möllenhoff, and Daniel Cremers. Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading. CVPR 2018.
 Xintao Wang, Ke Yu, Chao Dong, and Chen-Change Loy. Recovering Realistic Texture in Image Super-resolution by Spatial Feature Modulation. CVPR 2018. [Paper]
 Zheng Hui, Xiumei Wang, and Xinbo Gao. Fast and Accurate Single Image Super-Resolution via Information Distillation Network. CVPR 2018. [Paper]
 Xin Yu, Basura Fernando, Richard Hartley, and Fatih Porikli. Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes. CVPR 2018. [Paper]
 Wei Han, Shiyu Chang, Ding Liu, Michael Witbrock, and Thomas Huang. Image Super-resolution via Dual-state Recurrent Neural Networks. CVPR 2018. [Paper]
 Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, and Jian Yang. FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors. CVPR 2018. [Paper]
 Ying Qu, Hairong Qi, and Chiman Kwan. Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution. CVPR 2018.
 Assaf Shocher, Nadav Cohen, and Michal Irani. “Zero-Shot” Super-Resolution using Deep Internal Learning. CVPR 2018. [Paper]
 Younghyun Jo, Seoung Wug Oh, JaeYeon Kang, and Seon Joo Kim. Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation. CVPR 2018.
 Weimin Tan, Bo Yan, and Bahetiyaer Bare. Feature Super-Resolution: Make Machine See More Clearly. CVPR 2018.
 Mehdi S. M. Sajjadi, Raviteja Vemulapalli, and Matthew Brown. Frame-Recurrent Video Super-Resolution. CVPR 2018. [Paper]
 Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers. A Fully Progressive Approach to Single-Image Super-Resolution. arXiv, 2018. [Paper]
 Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue. Deep Learning for Single Image Super-Resolution: A Brief Review. arxiv, 2018. [Paper] (a survey paper)
 Adrian Bulat, Jing Yang, Georgios Tzimiropoulos. To learn image super-resolution, use a GAN to learn how to do image degradation first. ECCV, 2018. [Paper]
 Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn. Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network. ECCV 2018. [Paper]
 Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang. Enhanced Super-Resolution Generative Adversarial Networks. ECCV2018 PIRM Workshop. [Code]
 Seong-Jin Park, Hyeongseok Son, Sunghyun Cho, Ki-Sang Hong. SRFeat: Single Image Super-Resolution with Feature Discrimination. ECCV 2018. [Paper]
 Subeesh Vasu, Nimisha T. M., A. N. Rajagopalan. Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network. ECCV2018 PIRM Workshop. [Code]
 Vu, Thang and Van Nguyen, Cao and Pham, Trung X. and Luu, Tung M. and Yoo, Chang Dong. Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks. ECCV2018 PIRM Mobile Workshop [Paper] [Code]
 Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, Chen Change Loy. Deep Network Interpolation for Continuous Imagery Effect Transition. CVPR 2019. [Website]
 Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Jian Sun, Tieniu Tan. Meta-SR: A Magnification-Arbitrary Network for Super-Resolution. ArXiv, 2019. [Paper]
 Zhang, Kai and Zuo, Wangmeng and Zhang, Lei. Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels, CVPR 2019. [Project]
 Xuaner Zhang, Qifeng Chen, Ren Ng, and Vladlen Koltun. Zoom to Learn, Learn to Zoom, CVPR 2019. [Paper]