Awesome
KinD
This is a Tensorflow implementation of KinD
The KinD++ is an improved version.
Kindling the Darkness: a Practical Low-light Image Enhancer. In ACMMM2019<br> Yonghua Zhang, Jiawan Zhang, Xiaojie Guo
Paper
<img src="figures/network.jpg" width="800px"/> <img src="figures/result.jpg" width="800px"/>To better adapt to TensorFlow 2.0, we modified our code.Now you can run this code directly using TensorFlow 2.0.
Requirements
- Python
- Tensorflow >= 2.0
- numpy, PIL
Test
First download the pre-trained checkpoints from BaiduNetdisk or google drive, then just run
python evaluate.py
Our pre-trained model has changed. Thus, the results have some difference with the report in our paper. However, you can adjust the illumination ratio to get better results.
Train
The original LOLdataset can be downloaded from here. We rearrange the original LOLdataset and add several all-zero images to improve the decomposition results and restoration results. The new dataset can be download from BaiduNetdisk or google drive. Save training pairs of LOL dataset under './LOLdataset/our485/' and save evaluating pairs under './LOLdataset/eval15/'. For training, just run
python decomposition_net_train.py
python adjustment_net_train.py
python reflectance_restoration_net_train.py
You can also evaluate on the LOLdataset, just run
python evaluate_LOLdataset.py
Our code partly refers to the code.
Citation
@inproceedings{zhang2019kindling,
author = {Zhang, Yonghua and Zhang, Jiawan and Guo, Xiaojie},
title = {Kindling the Darkness: A Practical Low-light Image Enhancer},
booktitle = {Proceedings of the 27th ACM International Conference on Multimedia},
series = {MM '19},
year = {2019},
isbn = {978-1-4503-6889-6},
location = {Nice, France},
pages = {1632--1640},
numpages = {9},
url = {http://doi.acm.org/10.1145/3343031.3350926},
doi = {10.1145/3343031.3350926},
acmid = {3350926},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {image decomposition, image restoration, low light enhancement},
}