Awesome

<div align="center"> Multi-scale Attention Network for Single Image Super-Resolution </div>

Yan Wang^†, Yusen Li^†, Gang Wang, Xiaoguang Liu

Overview: To unleash the potential of ConvNet in super-resolution, we propose a multi-scale attention network (MAN), by coupling a classical multi-scale mechanism with emerging large kernel attention. In particular, we proposed multi-scale large kernel attention (MLKA) and gated spatial attention unit (GSAU). Experimental results illustrate that our MAN can perform on par with SwinIR and achieve varied trade-offs between state-of-the-art performance and computations.

This repository contains PyTorch implementation for MAN (CVPRW 2024).

1. Requirements
2. Datasets
3. Implementary Details
4. Train and Test
5. Results and Models
6. Acknowledgments
7. Citation

⚙️ Requirements

• PyTorch >= 1.8
• BasicSR >= 1.3.5

🎈 Datasets

Training: DIV2K or DF2K.

Testing: Set5, Set14, BSD100, Urban100, Manga109 (Google Drive/Baidu Netdisk).

Preparing: Please refer to the Dataset Preparation of BasicSR.

🔎 Implementary Details

Network architecture: Group number (n_resgroups): 1 for simplicity, MAB number (n_resblocks): 5/24/36, channel width (n_feats): 48/60/180 for tiny/light/base MAN.

Component details: Three multi-scale decomposition modes are utilized in MLKA. The 7×7 depth-wise convolution is used in the GSAU.

▶️ Train and Test

The BasicSR framework is utilized to train our MAN, also testing.

Training with the example option

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/trian_MAN.yml --launcher pytorch

Testing with the example option

python test.py -opt options/test_MAN.yml

The training/testing results will be saved in the ./experiments and ./results folders, respectively.

📊 Results and Models

Pretrained models available at Google Drive and Baidu Netdisk (pwd: mans for all links).

Results of our MAN-tiny/light/base models. Set5 validation set is used below to show the general performance. The visual results of five testsets are provided in the last column.

💖 Acknowledgments

We would thank VAN and BasicSR for their enlightening work!

🎓 Citation

@inproceedings{wang2024multi,
  title={Multi-scale Attention Network for Single Image Super-Resolution},
  author={Wang, Yan and Li, Yusen and Wang, Gang and Liu, Xiaoguang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  year={2024}
}

or

@article{wang2022multi,
  title={Multi-scale Attention Network for Single Image Super-Resolution},
  author={Wang, Yan and Li, Yusen and Wang, Gang and Liu, Xiaoguang},
  journal={arXiv preprint arXiv:2209.14145},
  year={2022}
}