| 2019 | IEEE Access | End-to-end image super-resolution via deep and shallow convolutional networks | - | 42 |
| 2019 | arxiv | Deep learning for image super-resolution: A survey | - | 4 |
| 2019 | arxiv | Toward Real-World Single Image Super-Resolution:A New Benchmark and A New Model | - | 3 |
| 2019 | CVPR | Ntire 2019 challenge on real image denoising: Methods and results | - | 4 |
| 2019 | CVPR | Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels | Pytorch | 1 |
| 2019 | CVPR | Second-order Attention Network for Single Image Super-resolution | - | 0 |
| 2019 | CVPRW | Encoder-Decoder Residual Network for Real Super-Resolution | - | 1 |
| 2019 | CVPRW | EDVR: Video Restoration with Enhanced Deformable Convolutional Networks | Pytorch | 2 |
| 2018 | ECCV | Fast, accurate, and lightweight super-resolution with cascading residual network | - | 30 |
| 2018 | ECCV | Image super-resolution using very deep residual channel attention networks | - | 103 |
| 2018 | ECCV | To learn image super-resolution, use a GAN to learn how to do image degradation first | - | 17 |
| 2018 | ECCV | Face super-resolution guided by facial component heatmaps | - | 7 |
| 2018 | ECCV | Multi-scale residual network for image super-resolution | - | 11 |
| 2018 | ECCV | CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping | - | 5 |
| 2018 | ECCV | Srfeat: Single image super-resolution with feature discrimination | - | 9 |
| 2018 | ECCVW | The unreasonable effectiveness of texture transfer for single image super-resolution | - | 6 |
| 2018 | ECCVW | The 2018 PIRM challenge on perceptual image super-resolution | - | 38 |
| 2018 | ECCVW | PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report | - | 16 |
| 2018 | ECCVW | Esrgan: Enhanced super-resolution generative adversarial networks | PyTorch | 63 |
| 2018 | ECCV | Generative adversarial network-based image super-resolution using perceptual content losses | - | 3 |
| 2018 | CVPR | Fast and accurate single image super-resolution via information distillation network | Caffe | 43 |
| 2018 | CVPR | Image super-resolution via dual-state recurrent networks | Tensorflow | 24 |
| 2018 | CVPR | Deep back-projection networks for super-resolution | - | 101 |
| 2018 | CVPR | A fully progressive approach to single-image super-resolution | - | 28 |
| 2018 | CVPR | FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors | Torch | 36 |
| 2018 | CVPR | Residual dense network for image super-resolution | Torch | 153 |
| 2018 | CVPR | Recovering realistic texture in image super-resolution by deep spatial feature transform | - | 59 |
| 2018 | CVPR | Learning a single convolutional super-resolution network for multiple degradations | - | 59 |
| 2018 | CVPR | Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation | Tensorflow | 19 |
| 2018 | CVPR | Frame-recurrent video super-resolution | - | 29 |
| 2018 | CVPRW | Deep residual network with enhanced upscaling module for super-resolution | - | 9 |
| 2018 | CVPRW | Image super-resolution via progressive cascading residual network | - | 2 |
| 2018 | CVPRW | Ntire 2018 challenge on single image super-resolution: Methods and results | - | 45 |
| 2018 | CVPRW | [Persistent memory residual network for single image super resolution](Persistent Memory Residual Network for Single Image Super Resolution) | - | 3 |
| 2018 | TPAMI | Fast and accurate image super-resolution with deep laplacian pyramid networks | - | 55 |
| 2018 | TIP | LFNet: A novel bidirectional recurrent convolutional neural network for light-field image super-resolution | - | 11 |
| 2018 | TIP | Learning temporal dynamics for video super-resolution: A deep learning approach | - | 15 |
| 2018 | WACV | CT-SRCNN: cascade trained and trimmed deep convolutional neural networks for image super resolution | - | 6 |
| 2018 | arxiv | Single Image Super-Resolution via Cascaded Multi-Scale Cross Network | - | 9 |
| 2018 | arxiv | Wide Activation for Efficient and Accurate Image Super-Resolution | Pytorch | 13 |
| 2017 | ICLR | Amortised map inference for image super-resolution | - | 178 |
| 2017 | CVPR | Photo-realistic single image super-resolution using a generative adversarial network | - | 1884 |
| 2017 | CVPR | Enhanced deep residual networks for single image super-resolution | - | 445 |
| 2017 | CVPR | Image Super Resolution via Deep Recursive Residual Network | Matlab | 296 |
| 2017 | CVPR | Deep laplacian pyramid networks for fast and accurate super-resolution | Matlab | 378 |
| 2017 | CVPR | Image Super-Resolution via Deep Recursive Residual Network | Matlab | 296 |
| 2017 | CVPRW | Balanced two-stage residual networks for image super-resolution | Tensorflow | 18 |
| 2017 | CVPRW | Ntire 2017 challenge on single image super-resolution: Methods and results | - | 209 |
| 2017 | ICCV | Enhancenet: Single image super-resolution through automated texture synthesis | - | 167 |
| 2017 | ICCV | Pixel recursive super resolution | - | 96 |
| 2017 | ICCV | Image super-resolution using dense skip connections | - | 130 |
| 2017 | TIP | Deep edge guided recurrent residual learning for image super-resolution | - | 63 |
| 2017 | arxiv | Srpgan: Perceptual generative adversarial network for single image super resolution | - | 12 |
| 2017 | ICONIP | Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network | Tensorflow | 43 |
| 2016 | CVPR | Accurate image super-resolution using very deep convolutional networks | - | 103 |
| 2016 | CVPR | Deeply-recursive convolutional network for image super-resolution | - | 551 |
| 2016 | ECCV | Accelerating the super-resolution convolutional neural network | - | 493 |
| 2016 | TIP | Robust single image super-resolution via deep networks with sparse prior | - | 124 |
| 2015 | TPAMI | Image super-resolution using deep convolutional networks | - | 1876 |
| 2015 | ICCV | Deep Networks for Image Super-Resolution with Sparse Prior | - | 373 |