Home

Awesome

NEWS

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

Model types

  1. "DBPN" -> use T = 7
  2. "DBPNLL" -> use T = 10
  3. PIRM Model -> "DBPNLL" with adversarial loss
  4. "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    

DBPN

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}
}