|
|
---
|
|
|
license: mit
|
|
|
---
|
|
|
|
|
|
# Description
|
|
|
|
|
|
This is a collection of models supported by the hyperspectral image restoration (HSIR) toolbox, developed by Intelligent Sensing and Processing Laboratory (BIT ISP lab) at Beijing Institute of Technology.
|
|
|
|
|
|
The original repository is available at:
|
|
|
<https://github.com/bit-isp/HSIR>
|
|
|
and models were originally downloaded at the following link:
|
|
|
<https://1drv.ms/u/s!AuS3o7sEiuJnf6F4THmqDMtDCwQ?e=JpfLP3>
|
|
|
|
|
|
|
|
|
# Citation
|
|
|
|
|
|
To cite the original repository:
|
|
|
|
|
|
```
|
|
|
@misc{hsir,
|
|
|
author={Zeqiang Lai, Miaoyu Li, Ying Fu},
|
|
|
title={HSIR: Out-of-box Hyperspectral Image Restoration Toolbox},
|
|
|
year={2022},
|
|
|
url={https://github.com/bit-isp/HSIR},
|
|
|
}
|
|
|
```
|
|
|
For each of the model please cite:
|
|
|
|
|
|
- HSID-CNN
|
|
|
```bibtex
|
|
|
@ARTICLE{yuan2019,
|
|
|
author={Q. {Yuan} and Q. {Zhang} and J. {Li} and H. {Shen} and L. {Zhang}},
|
|
|
journal={IEEE Trans. Geosci. Remote Sens.},
|
|
|
title={Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network},
|
|
|
year={2019},
|
|
|
volume={57},
|
|
|
number={2},
|
|
|
pages={1205-1218},
|
|
|
month={Feb.},
|
|
|
}
|
|
|
```
|
|
|
- QRNN3D
|
|
|
```bibtex
|
|
|
@article{wei2020QRNN3D,
|
|
|
title={3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising},
|
|
|
author={Wei, Kaixuan and Fu, Ying and Huang, Hua},
|
|
|
journal={IEEE Transactions on Neural Networks and Learning Systems},
|
|
|
year={2020},
|
|
|
publisher={IEEE}
|
|
|
}
|
|
|
```
|
|
|
- TS3C
|
|
|
```bibtex
|
|
|
@article{bodrito2021trainable,
|
|
|
title={A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration},
|
|
|
author={Bodrito, Theo and Zouaoui, Alexandre and Chanussot, Jocelyn and Mairal, Julien},
|
|
|
journal={Adv. in Neural Information Processing Systems (NeurIPS)},
|
|
|
year={2021}
|
|
|
}
|
|
|
```
|
|
|
- GRUNet
|
|
|
```bibtex
|
|
|
@article{lai2022dphsir,
|
|
|
title = {Deep plug-and-play prior for hyperspectral image restoration},
|
|
|
journal = {Neurocomputing},
|
|
|
volume = {481},
|
|
|
pages = {281-293},
|
|
|
year = {2022},
|
|
|
issn = {0925-2312},
|
|
|
doi = {https://doi.org/10.1016/j.neucom.2022.01.057},
|
|
|
author = {Zeqiang Lai and Kaixuan Wei and Ying Fu},
|
|
|
}
|
|
|
```
|
|
|
|
|
|
- SST
|
|
|
```bibtex
|
|
|
@inproceedings{li2023spatial,
|
|
|
title={Spatial-Spectral Transformer for Hyperspectral Image Denoising},
|
|
|
author={Li, Miaoyu and Fu, Ying and Zhang, Yulun},
|
|
|
booktitle={AAAI},
|
|
|
year={2023}
|
|
|
}
|
|
|
```
|
|
|
- SERT
|
|
|
```bibtex
|
|
|
@inproceedings{li2023spectral,
|
|
|
title={Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising},
|
|
|
author={Miaoyu Li and Ji Liu and Ying Fu and Yulun Zhang and Dejing Dou},
|
|
|
booktitle={CVPR},
|
|
|
year={2023}
|
|
|
}
|
|
|
```
|
|
|
- MAN
|
|
|
```bibtex
|
|
|
@article{lai2023mixed,
|
|
|
title={Mixed Attention Network for Hyperspectral Image Denoising},
|
|
|
author={Lai, Zeqiang and Fu, Ying},
|
|
|
journal={arXiv preprint arXiv:2301.11525},
|
|
|
year={2023}
|
|
|
}
|
|
|
```
|
|
|
- HSDT
|
|
|
```bibtex
|
|
|
@inproceedings{lai2023hsdt,
|
|
|
author = {Lai, Zeqiang and Chenggang, Yan and Fu, Ying},
|
|
|
title = {Hybrid Spectral Denoising Transformer with Guided Attention},
|
|
|
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
|
|
|
year = {2023},
|
|
|
}
|
|
|
```
|
|
|
|
