Magic-ELF

September 13, 2023 ยท View on GitHub

Magic ELF: Image Deraining Meets Association Learning and Transformer (ACMMM2022)

Kui Jiang, Zhongyuan Wang, Chen Chen, Zheng Wang, Laizhong Cui, and Chia-Wen Lin

Paper: Magic ELF: Image Deraining Meets Association Learning and Transformer

Installation

The model is built in PyTorch 1.1.0 and tested on Ubuntu 16.04 environment (Python3.7, CUDA9.0, cuDNN7.5).

For installing, follow these intructions

conda create -n pytorch1 python=3.7
conda activate pytorch1
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=9.0 -c pytorch
pip install matplotlib scikit-image opencv-python yacs joblib natsort h5py tqdm

Quick Test

To test the pre-trained deraining model on your own images, run

python test.py

Training and Evaluation

Training

  • Download the Datasets

  • Train the model with default arguments by running

python train.py

Evaluation

  1. Download the model and place it in ./pretrained_models/

  2. Download test datasets (Test100, Rain100H, Rain100L, Test1200, Test2800) from here and place them in ./Datasets/Synthetic_Rain_Datasets/test/

  3. Run

python test.py

To reproduce PSNR/SSIM scores of the paper, run

evaluate_PSNR_SSIM.m

Results

Experiments are performed for different image processing tasks including, image deraining, image dehazing and low-light image enhancement.

Acknowledgement

Code borrows from MPRNet by Syed Waqas Zamir. Thanks for sharing !

Citation

If you use Magic ELF, please consider citing:

@article{jiangpcnet,
    title={Magic ELF: Image Deraining Meets Association Learning and Transformer},
    author={Kui Jiang and Zhongyuan Wang and Chen Chen and Zheng Wang and Laizhong Cui and Chia-Wen Lin},
    journal={ACMMM}, 
    year={2022}
}

Contact

Should you have any question, please contact Kui Jiang (kuijiang@whu.edu.cn)