Official Code for: Guanyao Wu, Hongming Fu, Jinyuan Liu, Long Ma, Xin Fan, Risheng Liu*, "Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-Free Multi-Exposure Image Fusion", in Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024.
We strongly recommend that you use Conda as a package manager.
# create virtual environment
conda create -n hsds python=3.8
conda activate hsds
# select and install pytorch version yourself (Necessary & Important)
# install requirements package
pip install -r requirements.txtThis code natively supports the same naming for over-/under-exposed image pairs. An naming example can be found in ./Data/test folder.
# Test: use given example and save fused color images to ./Data/test/result.
# If you want to test the custom data, please modify the file path in 'test.py'.
python test.pypython train_search.pyFind the string "arch" and "hyper" in S_EXP.log that describes the searched architectures and weights of loss funtions. Copy and paste it into the "./model/genotypes.py".
python train.pyIf this work has been helpful to you, we would appreciate it if you could cite our paper!
@inproceedings{wu2024hybrid,
title={Hybrid-supervised dual-search: Leveraging automatic learning for loss-free multi-exposure image fusion},
author={Wu, Guanyao and Fu, Hongming and Liu, Jinyuan and Ma, Long and Fan, Xin and Liu, Risheng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={6},
pages={5985--5993},
year={2024}
}