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