[]() "Second-order Attention Network for Single Image Super-resolution" is published on CVPR-2019. The code is built on RCAN(pytorch) and tested on Ubuntu 16.04 (Pytorch 0.4.0)
BI degradation, scale 2, 3, 4,8
input= 48x48, output = 96x96
python main.py --model san --save
save_name
--scale 2 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --patch_size 96input= 48x48, output = 144x144
python main.py --model san --save
save_name
--scale 3 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --patch_size 96input= 48x48, output = 192x192
python main.py --model san --save
save_name
--scale 4 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --patch_size 96input= 48x48, output = 392x392
python main.py --model san --save
save_name
--scale 8 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --patch_size 96
BI degradation, scale 2, 3, 4,8
SAN_2x
python main.py --model san --data_test MyImage --save
save_name
--scale 2 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5 --pre_train ../model/SAN_BIX2.ptSAN_3x
python main.py --model san --data_test MyImage --save
save_name
--scale 3 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5 --pre_train ../model/SAN_BIX3.ptSAN_4x
python main.py --model san --data_test MyImage --save
save_name
--scale 4 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5 --pre_train ../model/SAN_BIX4.ptSAN_8x
python main.py --model san --data_test MyImage --save
save_name
--scale 8 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5 --pre_train ../model/SAN_BIX8.pt4. Results
- Some of the test results can be downloaded. Password:w3da
If the the work or the code is helpful, please cite the following papers
@inproceedings{dai2019second,
title={Second-order Attention Network for Single Image Super-Resolution}, author={Dai, Tao and Cai, Jianrui and Zhang, Yongbing and Xia, Shu-Tao and Zhang, Lei}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={11065--11074}, year={2019} }
@inproceedings{zhang2018image,
title={Image super-resolution using very deep residual channel attention networks}, author={Zhang, Yulun and Li, Kunpeng and Li, Kai and Wang, Lichen and Zhong, Bineng and Fu, Yun}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, pages={286--301}, year={2018} }
@inproceedings{li2017second, title={Is second-order information helpful for large-scale visual recognition?}, author={Li, Peihua and Xie, Jiangtao and Wang, Qilong and Zuo, Wangmeng}, booktitle={Proceedings of the IEEE International Conference on Computer Vision}, pages={2070--2078}, year={2017} }
The code is built on RCAN (Pytorch) and EDSR (Pytorch). We thank the authors for sharing the codes.