README.md

📋 Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer With Adaptive Channel Expansion

Shenghong Luo ^{👨‍💻‍} , Xuhang Chen ^{👨‍💻‍} , Weiwen Chen , Zinuo Li , Shuqiang Wang ^📮 and Chi-Man Pun ^📮 ( 👨‍💻‍ Equal contributions, 📮 Corresponding authors)

University of Macau, SIAT CAS, Huizhou University

2024 AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI 2024)

Project | Code | VigSet (Kaggle)

Dataset

VigSet stands as the only large-scale high-resolution dataset that includes ground truth data specifically for the task of vignetting correction.

Usage

Training

You may download the dataset first, and then specify TRAIN_DIR, VAL_DIR and SAVE_DIR in the section TRAINING in config.yml.

For single GPU training:

python train.py

For multiple GPUs training:

accelerate config
accelerate launch train.py

If you have difficulties with the usage of accelerate, please refer to Accelerate.

Inference

Please first specify TRAIN_DIR, VAL_DIR and SAVE_DIR in section TESTING in config.yml.

python infer.py

Acknowledgement

This work was supported in part by the Science and Technology Development Fund, Macau SAR, under Grant 0087/2020/A2 and Grant 0141/2023/RIA2, in part by the National Natural Science Foundations of China under Grant 62172403, in part by the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, in part by the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211.

Citation

@article{Luo_Chen_Chen_Li_Wang_Pun_2024, 
    title={Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer with Adaptive Channel Expansion}, 
    volume={38}, 
    DOI={10.1609/aaai.v38i5.28193}, 
    number={5}, 
    journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
    author={Luo, Shenghong and Chen, Xuhang and Chen, Weiwen and Li, Zinuo and Wang, Shuqiang and Pun, Chi-Man}, 
    year={2024}, 
    month={Mar.}, 
    pages={4000-4008} 
}