LBNet-Pytorch: Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer

Oficial PyTorch implementation of the paper “Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer”.

Performance

Our LBNet is trained on RGB, but as in previous work, we only reported PSNR/SSIM on the Y channel.

Model	Scale	Params	Multi-adds	Set5	Set14	B100	Urban100	Manga109
LBNet-T	x2	404K	49.0G	37.95/0.9602	33.53/0.9168	32.07/0.8983	31.91/0.9253	38.59/0.9768
LBNet	x2	731K	153.2G	38.05/0.9607	33.65/0.9177	32.16/0.8994	32.30/0.9291	38.88/0.9775
LBNet-T	x3	407K	22.0G	34.33/0.9264	30.25/0.8402	29.05/0.8042	28.06/0.8485	33.48/0.9433
LBNet	x3	736K	68.4G	34.47/0.9277	30.38/0.8417	29.13/0.8061	28.42/0.8559	33.82/0.9460
LBNet-T	x4	410K	12.6G	32.08/0.8933	28.54/0.7802	27.54/0.7358	26.00/0.7819	30.37/0.9059
LBNet	x4	742K	38.9G	32.29/0.8960	28.68/0.7832	27.62/0.7382	26.27/0.7906	30.76/0.9111

Model complexity

LBNet gains a better trade-off between model size, performance, inference speed, and multi-adds.

Acknowledgements

This code is built on EDSR (PyTorch) and DRN. We thank the authors for sharing their codes.

Citation

If you use any part of this code in your research, please cite our paper:

@article{gao2022lightweight ,
  title={Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer},
  author={Gao, Guangwei and Wang, Zhengxue and Li, Juncheng and Li, Wenjie and Yu, Yi and Zeng, Tieyong},
  journal={arXiv preprint arXiv:2204.13286},
  year={2022}
}