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]