ColinTaoZhang / HyperReconNet

HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging

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HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging

This is Caffe implementation of HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging in IEEE TIP, 2019


Paper


Dependence

This implemented is based on the modified Caffe.

Citation

If you use our dataset or code for research, please ensure that you cite our paper:

@ARTICLE{HyperReconNet,
  author={Wang, Lizhi and Zhang, Tao and Fu, Ying and Huang, Hua},
  journal={IEEE Transactions on Image Processing}, 
  title={HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging}, 
  year={2019},
  volume={28},
  number={5},
  pages={2257-2270}
}

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HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging


Languages

Language:Python 55.5%Language:C++ 23.4%Language:Cuda 21.1%