Steps for the public CAVE dataset:
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Run cave_processing.py to process the data;
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Run blurring_image.m to blurring the raw hyperspectral images;
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Run main.sh to test our method.
For our real-world dataset (./data/Hide/):
Simply run main_real.sh to test our method.
The trained parameters of the B3DDN is stored in ./models/hsidb_epoch500.pkl, if you want to train the B3DDN by yourself:
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Run cave_processing.py to process the data;
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Run train.py.
For any questions, feel free to email me at xiuheng.wang@oca.eu.
If this code is helpful for you, please cite our paper as follows:
@article{wang2023tuning,
title={Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors},
author={Wang, Xiuheng and Chen, Jie and Richard, C{\'e}dric},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2023}
}
@inproceedings{wang2020learning,
title={Learning Spectral-Spatial Prior Via 3DDNCNN for Hyperspectral Image Deconvolution},
author={Wang, Xiuheng and Chen, Jie and Richard, C{\'e}dric and Brie, David},
booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={2403--2407},
year={2020},
organization={IEEE}
}