This is a Keras implementation of “MBLLEN: Low-light Image/Video Enhancement Using CNNs” in BMVC 2018, by Feifan Lv, Feng Lu, Jianhua Wu and Chongsoon Lim. This page provides more experiments on real low-light images compared with latest methods.
Paper and Project page
To quickly test your own low-light images with our model, you can just run through
cd main
python test.py -i <input folder> -r <output folder> -m <model name>
By default, the code takes the data in the "../input/" folder, loads the "Syn_img_lowlight_withnoise.h5" model and saves results in the "../result/" folder. Please read the code to see other parameter settings.
First, prepare your own dataset or download our synthetic low-light dataset from our Project page. Second, change the load images path of "train.py" and "data_load.py". Then, you can just run through
cd main
python train.py
By default, the code takes the data in the "../dataset/" folder and save weights in the "./models/" folder. Please read the code to see other parameter settings.
To obtain better enhancement result, we linearly amplify the output of the network to improve contrast. Please read the code to see other parameter settings.
Our LOL fine-tuned version performs well on LOL test images.
Our model is comparable with DeepUPE. Notice that, our models are not fine-tuned using DeepUPE's images (training images are not provided).
Coming Soon ...
If you use this code for your research, please cite our paper.
@inproceedings{Lv2018MBLLEN,
title={MBLLEN: Low-light Image/Video Enhancement Using CNNs},
author={Feifan Lv, Feng Lu, Jianhua Wu, Chongsoon Lim},
booktitle={British Machine Vision Conference (BMVC)},
year={2018}
}
Feifan Lv, Yu Li and Feng Lu. Attention-guided Low-light Image Enhancement. arXiv:1908.00682, 2019. Paper and Project page