Awesome
EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network
Introduction
This project page provides TensorFlow 1.X code that implements the following AAAI2019 paper:
Title: "EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network"
Paper: https://aaai.org/ojs/index.php/AAAI/article/view/7013/6867
How to use
conda env create --prefix ./env --file environment.yml
conda activate .env
pip install h5py
pip install rawpy
Download pretrained model
# model directory structure
result
|-- Sony_edge
|-- Sony_merge
|-- Fuji_edge
└-- Fuji_merge
Process data
Please download raw dataset from https://github.com/cchen156/Learning-to-See-in-the-Dark
And delete misalignment images
Then process data as follows (about two hours)
python dataset.py
# dataset directory structure
dataset
|-- Fuji
| |-- fuji_long.hdf5
| |-- fuji_short.hdf5
| |-- long
| └-- short
└-- Sony
|-- sony_long.hdf5
|-- sony_short.hdf5
|-- long
└-- short
Training
Please modify the name of target dataset first
python train_fusion.py
python train_merge.py
Evaluation
python test.py
Performance
Model | Sony (PSNR) | Sony (SSIM) | Fuji (PSNR) | Fuji (SSIM) |
---|---|---|---|---|
baseline | 29.06 | 0.787 | 26.95 | 0.717 |
MEF | 29.43 | 0.791 | 27.21 | 0.719 |
EEMEFN (paper) | 29.60 | 0.795 | 27.38 | 0.723 |
EEMEFN (pretrained model) | 29.60 | 0.795 | 27.43 | 0.723 |
License
This code is released under the MIT License (refer to the LICENSE file for details).