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Contourlet Residual for Prompt Learning Enhanced Infrared Image Super-Resolution (CoRPLE)

This repo is the official implementation of, “Contourlet Residual for Prompt Learning Enhanced Infrared Image Super-Resolution”, Xingyuan Li, Jinyuan Liu*, Zhixin Chen, Yang Zou, Long Ma, Xin Fan, Risheng Liu, European Conference on Computer Vision (ECCV), 2024.

[pretrained models] [paper link]

🤖 Download

Download our datasets of infrared image super-resolution with detection labels. Original images are provided by TarDAL.

Download our datasets of infrared image super-resolution with segmentation labels. Original images are provided by SegMiF.

Dependencies

# Clone the github repo and go to the default directory 'CoRPLE'.
git clone https://github.com/hey-it-s-me/CoRPLE.git
conda create -n CoRPLE python=3.8
conda activate CoRPLE
pip install -r requirements.txt
python setup.py develop

Training

Testing

Acknowledgements

This code is built on DAT and Contourlet-CNN .

Citation

If this work has been helpful to you, please feel free to cite our paper!

@inproceedings{li2024contourlet,
  title={Contourlet Residual for Prompt Learning Enhanced Infrared Image Super-Resolution},
  author={Li, Xingyuan and Liu, Jinyuan and Chen, Zhixin and Zou, Yang and Ma, Long and Fan, Xin and Liu, Risheng},
  booktitle={European Conference on Computer Vision},
  pages={270--288},
  year={2024}
}
}