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: http://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN.html
Pretrained models can be downloaded from this link! https://drive.google.com/drive/folders/1ahbeoEHkjxoo4NV1wReOmpoRWbl448z-?usp=sharing
It contains 4 files: (1) DBPN_x2.pth, (2) DBPN_x4.pth, (3) DBPN_x8.pth are from the original architecture (CVPR2018).
(4) NTIRE2018_x8.pth is used for NTIRE2018 competition (Track 1: Classic Bicubic x8)
We also provide original Caffe implementation
##########HOW TO##########
#Training
python3 main.py
#Testing
python3 eval.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}
}