AOD-Net

Introducion

This project is a pytorch implementation of AOD-Net : All-in-One Network for Dehazing. Recently, there are a number of good implementations:

Boyiliee/AOD-Net the original author's project, developed based on pycaffe, only have pre-trained model and inference code
TheFairBear/PyTorch-Image-Dehazing developed based on pytorch
weber0522bb/AODnet-by-pytorch developed based on pytorch
https://github.com/sachinpuranik99/AOD_Net developed based on keras

During our implementing, we referred the above implementations. However, our implementation has several unique and new features compared with the above implementations:

It has both train and test code We provided both train and test code and pre-trained pytorch pickle file
It has more clearly code structure We refactore the code structure of the dataloader to make it more consistent with pytorch dataset
It has better logging decorator We add logging decorator for network pipeline function calling

Preparation

First of all, clone the code

git clone https://github.com/walsvid/AOD-Net.pytorch.git

Then, install prerequisites

pip install -r requirements.txt

Data Preparation

Please download the training images and original images from author's web page.

Then make a directory for data, change the parameters about data directories.

Pre-trained model

Please download the pretrained model from this download link.

Train

chmod +x run_train.sh
./run_train.sh

You can change the parameter in train bash script to satisfied your project.

Test

chmod +x run_test.sh
./run_test.sh

Demo

This is the dehazing result image comparison. Left image is haze image, right image is clean image processed by AOD-Net.

TODO

Integrate existing models with detection tasks

Citation

If you using this project in your work, please don't forget to cite the original author's paper.

@inproceedings{li2017aod,
  title={Aod-net: All-in-one dehazing network},
  author={Li, Boyi and Peng, Xiulian and Wang, Zhangyang and Xu, Jizheng and Feng, Dan},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  volume={1},
  number={4},
  pages={7},
  year={2017}
}