TABLE OF CONTENTS
Light Side is an low-light image enhancement library that consist state-of-the-art deep learning methods. The light side of the Force is referenced. The aim is to create a light structure that will find the Light Side of the Night.
The light side of the Force, also known as Ashla, was one of two methods of using the Force. The light side was aligned with calmness, peace, and passiveness, and was used only for knowledge and defense. The Jedi were notable practitioners of the light, being selfless servants of the will of the Force, and their enemies, the Sith followed the dark side of the Force.
Source: Wookieepedia
Low-light image enhancement aims at improving the perception or interpretability of an image captured in an environment with poor illumination.
Source: paperswithcode
Before you begin, ensure you have met the following requirements:
| requirement | version |
|---|---|
| imageio | ~=2.15.0 |
| numpy | ~=1.22.0 |
| pytorch_lightning | ~=1.7.0 |
| scikit-learn | ~=1.0.2 |
| torch | ~=1.9.1 |
To install Light Side, follow these steps:
pip install light_sidegit clone https://github.com/canturan10/light_side.git
cd light_side
pip install .git clone https://github.com/canturan10/light_side.git
cd light_side
pip install -e ".[all]"import imageio
import light_side as ls
img = imageio.imread("test.jpg")
model = ls.Enhancer.from_pretrained("model_config_dataset")
model.eval()
results = model.predict(img)- Available Models
- Available Versions for a Spesific Model
- Latest Version for a Spesific Model
- Pretrained Model
- Model with Random Weight Initialization
- Pretrained Arch Model
- Arch Model with Random Weight Initialization
For more information, please refer to the APIs
For more information, please refer to the Architectures
For more information, please refer to the Datasets
For more information, please refer to the Deployment
To training, follow these steps:
For installing Light Side, please refer to the Installation.
python training/zerodce_training.pyFor optional arguments,
python training/zerodce_training.py --helpDuring development, you might like to have tests run.
Install dependencies
pip install -e ".[test]"pytest light_side --pylint --pylint-error-types=EFpytest light_side --doctest-modulespytest --doctest-modules --cov light_side --cov-report termTo contribute to Light Side, follow these steps:
- Fork this repository.
- Create a branch:
git checkout -b <branch_name>. - Make your changes and commit them:
git commit -m '<commit_message>' - Push to the original branch:
git push origin - Create the pull request.
Alternatively see the GitHub documentation on creating a pull request.
 
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If you want to contact me you can reach me at can.turan.10@gmail.com.
This project is licensed under MIT license. See LICENSE for more information.
The references used in the development of the project are as follows.
Click to expand!
@misc{Turan_satellighte,
author = {Turan, Oguzcan},
title = {{satellighte}},
url = {https://github.com/canturan10/satellighte}
}@article{DBLP:journals/corr/abs-2001-06826,
author = {Chunle Guo and
Chongyi Li and
Jichang Guo and
Chen Change Loy and
Junhui Hou and
Sam Kwong and
Runmin Cong},
title = {Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement},
journal = {CoRR},
volume = {abs/2001.06826},
year = {2020},
url = {https://arxiv.org/abs/2001.06826},
eprinttype = {arXiv},
eprint = {2001.06826},
timestamp = {Sat, 23 Jan 2021 01:20:17 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2001-06826.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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