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 |