$\hat{N}$-Net

Introduction

This is the source code for our paper: Hyperspectral image denoising via spectral noise distribution bootstrap.

Usage

1. Requirements

• Python =3.7
• torch =1.9.0, torchnet, torchvision
• 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

3. Training

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

4. Testing

• Testing on Synthetic data or Real HSIs with the pre-trained model

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

Citation

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

@article{Pan2023hypersepctral,
        title = {Hyperspectral image denoising via spectral noise distribution bootstrap},
        author = {Erting Pan and Yong Ma and Xiaoguang Mei and Fan Fan and Jiayi Ma},
        journal = {Pattern Recognition},
        volume = {142},
        pages = {109699},
        year = {2023},
        issn = {0031-3203},
        doi = {https://doi.org/10.1016/j.patcog.2023.109699}
        }

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)