Hyperspectral Image Destriping and Denoising from a Task Decomposition View

Introduction

This is the source code for our paper: "Hyperspectral Image Destriping and Denoising from a Task Decomposition View".[url]

Usage

1. Requirements

• Python =3.7
• torch =1.9.0, torchnet, torchvision
• pytorch_wavelets
• pickle, tqdm, tensorboardX, scikit-image

2. Data Preparation

• download ICVL hyperspectral image database from here

  save the data in *.mat format into your folder

• generate data with synthetic noise for training and validation

     # change the data folder first
      python  ./data/datacreate.py

• download Real HSI data

  GF5-baoqing dataset

  GF5-wuhan dataset

3. Training

   python main.py -a phd --dataroot (your data root) --phase train

4. Testing

• Testing on Synthetic data with the pre-trained model

      python  main.py -a sldr --phase valid  -r -rp checkpoints/model_best.pth

• Testing on Real HSIs with the pre-trained model

      python main.py -a sldr --phase test  -r -rp checkpoints/model_best.pth

Citation

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

@article{pan2023hyperspectral,
  title={Hyperspectral image destriping and denoising from a task decomposition view},
  author={Pan, Erting and Ma, Yong and Mei, Xiaoguang and Huang, Jun and Chen, Qihai and Ma, Jiayi},
  journal={Pattern Recognition},
  volume={144},
  pages={109832},
  year={2023},
  publisher={Elsevier}
}

Contact

Feel free to open an issue if you have any question. You could also directly contact us through email at panerting@whu.edu.cn (Erting Pan)