Zero-DCE TF

The Tensorflow Implementation of the Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement - CVPR 2020

Update:

I pushed my project to Google Cloud Platform. May need more improvement. Should you have any comment or inquiries or just basically want to enhance your images, give it a try here

Content

• Zero-DCE
• Getting Started
• Running
• References
• Citations
• Try Yourself

Getting Started

• Clone the repository

Prerequisites

• Tensorflow 2.2.0+
• Python 3.6+
• Keras 2.3.0
• PIL
• numpy

pip install -r requirements.txt

Running

Training

• Preprocess

  • Download the training data at Google Drive.

  • Run this file to generate data. (Please remember to change path first)

  python src/prepare_data.py
  

• Train ZERO_DCE

  python train.py
  

• Test ZERO_DCE

  python test.py
  

Usage

Training

python train.py [-h] [--lowlight_images_path LOWLIGHT_IMAGES_PATH] [--lr LR]
                [--num_epochs NUM_EPOCHS] [--train_batch_size TRAIN_BATCH_SIZE]
                [--val_batch_size VAL_BATCH_SIZE] [--display_iter DISPLAY_ITER]
                [--checkpoint_iter CHECKPOINT_ITER] [--checkpoints_folder CHECKPOINTS_FOLDER]
                [--load_pretrain LOAD_PRETRAIN] [--pretrain_dir PRETRAIN_DIR]

optional arguments: -h, --help                show this help message and exit
                    --lowlight_images_path    LOWLIGHT_IMAGES_PATH
                    --lr                      LR
                    --num_epochs              NUM_EPOCHS
                    --train_batch_size        TRAIN_BATCH_SIZE
                    --val_batch_size          VAL_BATCH_SIZE
                    --display_iter            DISPLAY_ITER
                    --checkpoint_iter         CHECKPOINT_ITER
                    --checkpoints_folder      CHECKPOINTS_FOLDER
                    --load_pretrain           LOAD_PRETRAIN
                    --pretrain_dir            PRETRAIN_DIR

Testing

python test.py [-h] [--lowlight_test_image_path]

optional arguments: -h, --help                    show this help message and exit
                    --lowlight_test_image_path    LOWLIGHT_TEST_IMAGES_PATH

Result

input	output

License

This project is licensed under the MIT License - see the LICENSE file for details

References

[1] Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement - CVPR 2020 link

[3] Low-light dataset - link

Citation

    @misc{guo2020zeroreference,
    title={Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement},
    author={Chunle Guo and Chongyi Li and Jichang Guo and Chen Change Loy and Junhui Hou and Sam Kwong and Runmin Cong},
    year={2020},
    eprint={2001.06826},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Try on web

The project is now available on GCP. Give it a try

Acknowledgments

• This repo is the re-production of the original pytorch version
• Thank you for helping me to understand more about pains that tensorflow may cause.
• Final words:
  • Any ideas on updating or misunderstanding, please send me an email: [email protected]
  • If you find this repo helpful, kindly give me a star.