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AAAI 2023 Student Abstract and Poster Program Submission

LVRNet, short for Low-Visibility Restoration Network, is a method that can effectively recover high-quality images from degraded images taken in poor visual conditions. Although we have tested our work for two degrading factors combined: low-light and haze, you can use this codebase and run experiments for other degrading factors as well using the instructions given below.

git clone https://github.com/Achleshwar/lvrnet.git
cd lvrnet 
pip install -r requirements.txt

We have used public dataset AFO and generated our dataset - Low-Vis AFO, by adding low visibility conditions. You can download it here.

For a quick demo, you can use our pretrained weights and run them on a demo images using src/lvrnet-notebook.ipynb.

Download the pretrained weights from here and change model_wts path in the notebook.

## train from scratch
python train.py --epochs 50 --data_dir <path to dataset> --log_dir <path to save weights> --perloss --edgeloss --fftloss

If you find this work useful, please cite our paper:

@misc{pahwa2023lvrnet,
      title={LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility}, 
      author={Esha Pahwa and Achleshwar Luthra and Pratik Narang},
      year={2023},
      eprint={2301.05434},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

We would like to thank the authors of FFANet, NAFNet and MC-Blur for their codebase. We have used their codebase as a starting point for our work.

• Add results on OOD images
• Add link to dataset
• Add link to project page