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]

The implementation of the 'Contourlet Refinement Gate Framework' is available on the CRG branch.

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

• Google Drive

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

• Google Drive

• Python 3.8
• PyTorch 1.8.0
• NVIDIA GPU + CUDA

# 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

• Run the following scripts. The training configuration is in options/train/.

  python basicsr/train.py -opt options/Train/train_CoRPLE_light_x2.yml
  python basicsr/train.py -opt options/Train/train_CoRPLE_light_x4.yml
• The training experiment is in experiments/.

• Run the following scripts. The testing configuration is in options/test/.

  python basicsr/train.py -opt options/Test/my_test_CoRPLE_light_x2.yml
  python basicsr/train.py -opt options/Test/my_test_CoRPLE_light_x4.yml
• The output is in results/.

This code is built on DAT and Contourlet-CNN .

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},
  organization={Springer}
}