The details about our deep battery saver can be found in our paper.
This code is licensed under CC BY-NC-SA 4.0. Commercial usage is not permitted. If you use this code in a scientific publication, please cite the following paper:
@ARTICLE{yin_tmm20_pcce,
author={J. -L. {Yin} and B. -H. {Chen} and Y. -T. {Peng} and C. -C. {Tsai}},
journal={IEEE Transactions on Multimedia},
title={Deep Battery Saver: End-to-End Learning for Power Constrained Contrast Enhancement},
year={2021},
volume={23},
number={},
pages={1049-1059},
doi={10.1109/TMM.2020.2992962}}
- Python 3
- Anaconda 3
- Tensorflow >= 2.0.0 (CUDA version >= 10.0 if installing with CUDA. More details)
- Python packages: You can install the required libraries by the command
conda DeepBatterySaver -n recreated_env --file requirements.txt
. We checked this code on cuda-10.0 and cudnn-7.6.5.
- Windows 10
- Ubuntu 16.04/18.04
Might work under others, but didn't get to test any other OSs just yet.
- Clone this github repo.
git clone https://github.com/bigmms/deep_battery_saver
- Place your testing images in
./test
folder. (There are several sample images). - Then,
cd
todeep_battery_saver
and run following command for evaluation:
python main.py --source_path ./test/pills.jpg
- To run with different settings, add
--num_epochs
,--power_level
,--learning_rate
,--source_path
,--target_path
as you need. - The results are in
./results
folder.