Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-world Hazy Scenes

TGL-Net dehazing network for our Sensors 2021 paper "Z. Li, J. Zhang, R. Zhong, B. Bhanu, Y. Chen, Q. Zhang, H. Tang. Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-world Hazy Scenes."
It can be used for image dehazing / image defogging / image enhancement on light computating load machines.

Run

Demo using pre-trained model

$ sh demo -m './model' -t './testset/*' -s './result'

Testing on your own testset

$ sh demo -m './model' -t './your_dataroot/*' -s ./your_output_path

Requirement

1. Linux
2. python3.6, Tensorflow-gpu 1.14.0 and other common packages
3. NVIDIA GPU + CUDA CuDNN (CUDA 10.0)

Citing

The code is free for academic/research purpose. Please kindly cite our work in your publications if it helps your research.

@article{
  title={Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-world Hazy Scenes},
  author={Z. Li, J. Zhang, R. Zhong, B. Bhanu, Y. Chen, Q. Zhang, H. Tang},
  journal={Sensors 2021, 21(3), 960. MDPI, Basel, Switzerland. https://doi.org/10.3390/s21030960},
  year={2021}
}

Example