Awesome
NEWS
- Apr 1, 2020 -> NEW paper on Space-Time Super-Resolution STARnet (to appear in CVPR2020)
- Jan 10, 2019 -> Added model used for PIRM2018, and support Pytorch >= 1.0.0
- Mar 25, 2019 -> Paper on Video Super-Resolution RBPN (CVPR2019)
- Apr 12, 2019 -> Added Extension of DBPN paper and model.
Deep Back-Projection Networks for Super-Resolution (CVPR2018)
Winner (1st) of NTIRE2018 Competition (Track: x8 Bicubic Downsampling)
Winner of PIRM2018 (1st on Region 2, 3rd on Region 1, and 5th on Region 3)
Project page: https://alterzero.github.io/projects/DBPN.html
We also provide original Caffe implementation
Pretrained models and Results
Pretrained models (DBPNLL) and results can be downloaded from this link! https://drive.google.com/drive/folders/1ahbeoEHkjxoo4NV1wReOmpoRWbl448z-?usp=sharing
Dependencies
- Python 3.5
- PyTorch >= 1.0.0
Model types
- "DBPN" -> use T = 7
- "DBPNLL" -> use T = 10
- PIRM Model -> "DBPNLL" with adversarial loss
- "DBPN-RES-MR64-3" -> improvement of DBPN with recurrent process + residual learning
##########HOW TO##########
#Training
python3 main.py
#Testing
python3 eval.py
#Training GAN for PIRM2018
python3 main_gan.py
#Testing GAN for PIRM2018
python3 eval_gan.py
Citations
If you find this work useful, please consider citing it.
@inproceedings{DBPN2018,
title={Deep Back-Projection Networks for Super-Resolution},
author={Haris, Muhammad and Shakhnarovich, Greg and Ukita, Norimichi},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}
@article{DBPN2019,
title={Deep Back-Projection Networks for Single Imaage Super-Resolution},
author={Haris, Muhammad and Shakhnarovich, Greg and Ukita, Norimichi},
journal={arXiv preprint arXiv:1904.05677},
year={2019}
}