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Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution, TNNLS, 2023.

Jiaojiao Li, Songcheng Du, Rui song, Yunsong Li, and Qian Du.

Code for the paper: Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution.

<div align=center><img src="/Image/network.png" width="100%" height="100%"></div> Fig. 1: Network architecture of our accurate SIGnet for ssr.

Training and Test Process

Please prepare the training and test data as operated in the paper.
Run "train.py" to train the SIGnet.
Run "test.py" to test.
Download the pretrained model (Baidu Disk, code: abcd)).

References

If you find this code helpful, please kindly cite:

[1] Li J, Du S, Song R, et al. Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023.

[2] J. Li, S. Du, R. Song, C. Wu, Y. Li and Q. Du, "HASIC-Net: Hybrid Attentional Convolutional Neural Network With Structure Information Consistency for Spectral Super-Resolution of RGB Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5522515, doi: 10.1109/TGRS.2022.3142258.

[3] Li J, Du S, Wu C, et al. DRCR Net: Dense Residual Channel Re-Calibration Network With Non-Local Purification for Spectral Super Resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 1259-1268.

[4] Du S, Leng Y, Liang X, et al. Degradation Aware Unfolding Network for Spectral Super-Resolution[J]. IEEE Geoscience and Remote Sensing Letters, 2023.

Citation Details

BibTeX entry:

@article{li2023progressive,
  title={Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution},
  author={Li, Jiaojiao and Du, Songcheng and Song, Rui and Li, Yunsong and Du, Qian},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2023},
  publisher={IEEE}
}

@ARTICLE{9678983,
  author={Li, Jiaojiao and Du, Songcheng and Song, Rui and Wu, Chaoxiong and Li, Yunsong and Du, Qian},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={HASIC-Net: Hybrid Attentional Convolutional Neural Network With Structure Information Consistency for Spectral Super-Resolution of RGB Images}, 
  year={2022},
  volume={60},
  number={},
  pages={1-15},
  doi={10.1109/TGRS.2022.3142258}}

@InProceedings{Li_2022_CVPR,
    author    = {Li, Jiaojiao and Du, Songcheng and Wu, Chaoxiong and Leng, Yihong and Song, Rui and Li, Yunsong},
    title     = {DRCR Net: Dense Residual Channel Re-Calibration Network With Non-Local Purification for Spectral Super Resolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2022},
    pages     = {1259-1268}
}

@article{du2023degradation,
  title={Degradation Aware Unfolding Network for Spectral Super-Resolution},
  author={Du, Songcheng and Leng, Yihong and Liang, Xinyi and Li, Jiaojiao and Liu, Wei and Du, Qian},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2023},
  publisher={IEEE}
}

Licensing

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.