This project implements a CycleGAN-based method for image dehazing. It uses a paired dataset containing hazy images and their corresponding ground truth clear images to train the CycleGAN model. The trained model can then be used to dehaze new images.
- Python (>=3.6)
- PyTorch
- torchvision
- PIL
The testing script accepts several parameters to control its execution:
--test_data_directory: Path to the directory containing test hazy images.
--output_directory: Path for the directory where dehazed images will be saved.
--model_path: Path to the pre-trained CycleGAN model file. It's in the same folder as in testing script.
python3 image_dehazing_testing_script.pyThe script generates dehazed images saved in the specified output directory. Each dehazed image filename corresponds to the input hazy image filename.
Permission Errors: If you encounter permission errors when writing the output images, ensure the script has write access to the output directory or try running the script with elevated permissions.
Model Not Found: Verify the model file is placed correctly and the path provided to --model_path parameter is accurate.
Contributions are welcome! Please follow these steps to contribute:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Make your changes.
- Commit your changes (
git commit -m 'Add some feature'). - Push to the branch (
git push origin feature-branch). - Open a pull request.
Created by Ojasva Goyal - feel free to contact me at [email protected] for any questions or feedback.