RUAS
this is the official code for the paper "Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement"
Environment Preparing
python 3.6
pytorch 0.4.1
Testing
We provide different models which are trained from different datasets.
lol is trained from LOL dataset.
upe is trained from MIT5K dataset.
dark is trained from DarkFace dataset.
Finally, run test.py, the results will be saved in ./result/
python test.py
--data_path #The folder path of the picture you want to test
E:/test/
--model #The checkpoint name
lol or upe or dark
--save_path #The save path of the picture processed
./result/
Training
If you want to train your own model on a new dataset, run train.py.
Only low light images are needed.
The model will be saved in ./EXP/train/weights.pt
python train.py
Searching
Please get train set and valid set ready, and run train_search.py. Due to the data you used is different from ours, it is reasonable that the searched architecture is different from ours.
python train_search.py
Reference
If you find our work useful in your research please consider citing our paper:
@inproceedings{liu2021ruas,
title = {Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement},
author = {Risheng, Liu and Long, Ma and Jiaao, Zhang and Xin, Fan and Zhongxuan, Luo},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year = {2021}
}
A great thanks to DARTS for providing the basis for this code.