MAN

Codes for paper "Multi-scale Attention Network for Single Image Super-Resolution".

@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}
}

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Environment

• PyTorch >= 1.8
• BasicSR >= 1.3.5

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.

Overview of the proposed MAN constituted of three components: the shallow feature extraction module (SF), the deep feature extraction module (DF) based on multiple multi-scale attention blocks (MAB), and the high-quality image reconstruction module.

 

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

Details of Multi-scale Large Kernel Attention (MLKA), Gated Spatial Attention Unit (GSAU), and Large Kernel Attention Tail (LKAT).  

Training and Testing

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

Dataset:

• Training: DIV2K and Flickr2K.
• Testing: Set5, Set14, B100, Urban100, and Manga109.

Training with the example option:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 train.py -opt options/trian_MAN.yml --launcher pytorch

Tesing 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 Pan (pwd: mans for all links).

HR (x4)	MAN-tiny	EDSR-base+	MAN-light	EDSR+	MAN
Para/MAdds	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.

Methods	Params	Madds	PSNR/SSIM (x2)	PSNR/SSIM (x3)	PSNR/SSIM (x4)	Results
MAN-tiny	150K	8.4G	37.91/0.9603	34.23/0.9258	32.07/0.8930	x2/x3/x4
MAN-light	840K	47.1G	38.18/0.9612	34.65/0.9292	32.50/0.8988	x2/x3/x4
MAN+	8712K	495G	38.44/0.9623	34.97/0.9315	32.87/0.9030	x2/x3/x4

Acknowledgements

We would thank VAN and BasicSR for their enlightening work!