🌟 EMEF: Ensemble Multi-Exposure Image Fusion, AAAI 2023

• ✨This repository is the official PyTorch implementation of EMEF✨

Results

• Download our results from Google Drive.
• Download our results from Baidu Netdisk (code: emef).

Requirements

• Install python and pytorch correctly.
• Install other requirements.

Here is the example with the help of conda:

conda create -n emef python=3.10
conda activate emef
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt

Datasets

We train EMEF in SICE.

- SICE
  - train
    - gt
      - 001_00.png
      - 001_01.png
      - 001_02.png
      - 001_03.png
      - 002_00.png
      ...
    - oe
      - 1.png
      - 2.png
      ...
    - ue
      - 1.png
      - 2.png
      ...
  - test
    ...

We evaluate EMEF in MEFB.

- MEFB
  - oe
    - 1.png
    - 2.png
    ...
  - ue
    - 1.png
    - 2.png
    ...

Pretrained Model

Here we provide our pretrained model:

Google Drive | Baidu Netdisk (code: emef)

Get Started

• Start visdom for visualization:

python -m visdom.server

• Train Stage 1:

python train.py --dataroot {path_to_SICE}/train --name demo --model demo --gpu_ids 1 --display_port 8097

• Validate Stage 1:

python validation.py --dataroot {path_to_SICE}/test --name demo --model demo --phase test --no_dropout --epoch latest --gpu_ids 1

• Stage 2:

python optimize.py --dataroot {path_to_MEFB} --name demo --model optim --gpu_ids 1 --display_port 8097

• Get MEF-SSIM score (evaldata has 3 subdirs: fake, oe, ue):

python eval.py --dataroot {path_to_evaldata} --name eval --model eval --phase test

Evaluation

• We use the evaluation code from MEFB.
• We use a pytorch implementation of MEF-SSIM to optimize and evaluate our results.

Acknowledgements

Many thanks to MEFB and all of the open source MEF methods. EMEF can't live without their public available code. Many thanks to pix2pix for their excellent GAN framework.

Citation

If you find our work useful in your research, please cite with:

@article{liu2023emef,
  title={EMEF: Ensemble Multi-Exposure Image Fusion},
  volume={37}, 
  url={https://ojs.aaai.org/index.php/AAAI/article/view/25259}, 
  DOI={10.1609/aaai.v37i2.25259}, 
  number={2}, 
  journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
  author={Liu, Renshuai and Li, Chengyang and Cao, Haitao and Zheng, Yinglin and Zeng, Ming and Cheng, Xuan}, 
  year={2023}, 
  month={Jun.}, 
  pages={1710-1718} 
  }