Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing Paper

This repo contains the implementation of our proposed LD-Net, the prerequisite libraries, and link of the dataset we have used in our experiments. For testing purpose we also provide a trained LD-Net weights and test hazy images.

This code is written with Anaconda python 3.7. Install the Anaconda python 3.7 and clone the repo with the following command:

git clone https://github.com/hayatkhan8660-maker/Light-DehazeNet.git
cd Light-DehazeNet

This code requires the following libraries, install these libraries before testing th code.

• torch==1.9.1
• torchvision==0.10.1
• numpy==1.19.2
• PIL==8.2.0
• matplotlib==3.3.4

run pip install -r requirements.txt to install all the dependencies.

For training we have used the Synthetic Hazy Dataset generated by Boyi Li and his team for single image dehazing task. The dataset can be downloaded using the following links.

• The synthetic hazy images training set
• The original images training set

The structure of the dataset directory should be as follows:

data
├── original_images
│   ├── images
│       ├── data
│       ├── NYU2_1.jpg
│       ├── NYU2_2.jpg
│       ├── NYU2_3.jpg
│       ├── .....
├── training_images
│   ├── data
│       ├── data
│       ├── NYU2_1_1_2.jpg
│       ├── NYU2_1_1_3.jpg
│       ├── NYU2_1_2_1.jpg
│       ├── .....
   

Run the following command to retrain the LD-Net model on Synthic hazy images from NYU2 Hazy dataset.

python run_experiment.py -th data/training_images/data/ -to data/original_images/images/ -e 60 -lr 0.0001

For testing purpose, we provide two different inferencing scripts namely single_test_inference.py and muliple_test_inference.py. Here single_test_inference.py performs dehazing on single image, whereas muliple_test_inference.py dehaze a batch of images at once as per user's requirements.

To run the single_test_inference.py, type the following command in terminal:

python single_test_inference.py -i query_hazy_images/outdoor_synthetic/soh(5).jpg

To run the muliple_test_inference.py, type the following command in terminal:

python muliple_test_inference.py -td query_hazy_images/outdoor_natural/

Originally the LD-Net model is implemented in Python 3.7 and PyTorch 1.9.1 and trained on a PC having NVIDIA GTX 1060 6GB GPU with CUDA 10.0. If you want to retrain the LD-Net model or reproduce the dehazing results make sure the system meets the aforementioned experimental settings.

Oringinally the CVR module is written in matlab. For user's convenience, we are working to parse it to Python and soon will upload it to this repo.
For time being the CVR matlab version can be accessed with the follownig google drive link : (https://drive.google.com/file/d/12myYdZbZqegrORpzn62CEo_pqJwbNaU6/view?usp=sharing)

Please cite our paper, if you want to reproduce the results using this code.

@article{ullah2021light,
  title={Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing},
  author={Ullah, Hayat and Muhammad, Khan and Irfan, Muhammad and Anwar, Saeed and Sajjad, Muhammad and Imran, Ali Shariq and De Albuquerque, Victor Hugo C},
  journal={IEEE Transactions on Image Processing},
  year={2021},
  publisher={IEEE}
}

Ullah, H., Muhammad, K., Irfan, M., Anwar, S., Sajjad, M., Imran, A. S., & De Albuquerque, V. H. C., 
"Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing", 
IEEE Transactions on Image Processing, 2021.