DeepFuse
This repository contains an unofficial Pytorch implementation of the DeepFuse network for image fusion of extreme exposure image pairs, published in ICCV 2017.
Prerequisits
This release of DeepFuse implementation was tested in Google Colab with
- Python = 3.8
- PyTorch = 1.10.0
- torchvision = 0.11.1
Dataset
The dataset used for this experiment is the subset of the SICE dataset given here: https://github.com/ytZhang99/CF-Net
The SICE dataset can be found here: https://github.com/csjcai/SICE
Training
Folder structure
Place ground truth, over exposed, and under exposed images for training in the following folder structure.
- SICE_subset
- test_data
- GT
- OE
- UE
- train_data
- GT
- OE
- UE
- val_data
- GT
- OE
- UE
Training
python Main.py --train True --use_cuda True --trainset "./SICE_subset/train_data/"
Testing
Set the use_cuda
flag to False
if necessary.
python Main.py --train False --use_cuda True --testset "./SICE_subet/test_data/"
References
Acknowledgements
Thanks to Kede Ma et al. for their PyTorch implementation of the MEFSSIM metric from https://github.com/makedede/MEFNet