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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}
}