CRMEF

Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion

Fusion Reuslts and Chinese Version

The source images and fused results on three datasets are provided in link

中文版介绍提供在此链接 link

Welcome all comparision and disscussion! If you have any questions, please sending an email to "[email protected]"

Preview of CRMEF

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General MEF

Misalgined MEF

Set Up on Your Own Machine

Virtual Environment

• pytorch 1.2

Test / Train

# Test: use given example and save fused color images to result/SICE
# If you want to test the custom data, please modify the file path in 'test.py'
python test_single.py

# the lightweight model
python test_single_lightweight.py

# if you want to test the alignment
cd DCNv2
sh make.sh
python test_align.py

# Train: 
python train.py

Citation

If this work has been helpful to you, we would appreciate it if you could cite our paper!

@article{liu2023embracing,
  title={Searching a Compact Architecture for Robust Multi-Exposure Image Fusion},
  author={Liu, Zhu and Liu, Jinyuan and Wu, Guanyao and Chen, Zihang and Fan, Xin and Liu, Risheng},
  journal={IEEE TCSVT},
  year={2024}
}