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Multi-scale Attention Network for Single Image Super-Resolution

Yan Wang, Yusen Li, Gang Wang, Xiaoguang Liu

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).

Table of contents

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


⚙️ Requirements

🎈 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.


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).  

▶️ 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.

Methods Params FLOPs 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

💖 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}
}

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