MAN

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Multi-scale Attention Network for Single Image Super-Resolution

Nankai University

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).

⚙️ 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).

HR (x4)	MAN-tiny	EDSR-base+	MAN-light	EDSR+	MAN
Params/FLOPs	150K/8G	1518K/114G	840K/47G	43090K/2895G	8712K/495G

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}
}