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📖 SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution

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[Paper]   [Supp]  

Mingjun Zheng, Long Sun, Jiangxin Dong, and Jinshan Pan <br> IMAG Lab, Nanjing University of Science and Technology


<p align="center"> <img width="800" src="./figs/smfanet_arch.png"> </p>

Network architecture of the proposed SMFANet. The proposed SMFANet consists of a shallow feature extraction module, feature modulation blocks, and a lightweight image reconstruction module. Feature modulation block contains one self-modulation feature aggregation (SMFA) module and one partial convolution-based feed-forward network (PCFN).


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Requirements

Installation

# Clone the repo
git clone https://github.com/Zheng-MJ/SMFANet.git
# Install dependent packages
cd SMFANet
conda create --name smfan python=3.8
conda activate smfan
pip install -r requirements.txt
# Install BasicSR
python setup.py develop

You can also refer to this INSTALL.md for installation

Data Preparation

Please refer to datasets/REDAME.md for data preparation.

Training

Run the following commands for training:

# train SMFANet for x4 effieicnt SR
python basicsr/train.py -opt options/train/SMFANet/SMFANet_DIV2K_100w_x4SR.yml
# train SMFANet+ for x4 effieicnt SR
python basicsr/train.py -opt options/train/SMFANet/SMFANet_plus_DIV2K_100w_x4SR.yml

Testing

# test SMFANet for x4 efficient SR
python basicsr/test.py -opt options/test/SMFANet_DF2K_x4SR.yml

Pretrained Model & Visual Results

Google Drive | Huggingface

TensorRT Optimization

Hugging Face Demo

Plotting Script

Experimental Results

Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@inproceedings{smfanet,
    title={SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution},
    author={Zheng, Mingjun and Sun, Long and Dong, Jiangxin and Pan, Jinshan},
    booktitle={ECCV},
    year={2024}
 }

Acknowledgement

This code is based on BasicSR toolbox. Thanks for the awesome work.

Contact

If you have any questions, please feel free to reach me out at mingjunzheng@njust.edu.cn