HSCNN: CNN-Based Hyperspectral Image Recovery from Spectrally Undersampled Projections

@article{Xiong2017HSCNNCH,
  title={HSCNN: CNN-Based Hyperspectral Image Recovery from Spectrally Undersampled Projections},
  author={Zhiwei Xiong and Zhan Shi and Huiqun Li and Lizhi Wang and Dong Liu and Feng Wu},
  journal={2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},
  year={2017},
  pages={518-525}
}
This paper presents a unified deep learning framework to recover hyperspectral images from spectrally undersampled projections. Specifically, we investigate two kinds of representative projections, RGB and compressive sensing (CS) measurements. These measurements are first upsampled in the spectral dimension through simple interpolation or CS reconstruction, and the proposed method learns an end-to-end mapping from a large number of up-sampled/groundtruth hyperspectral image pairs. The mapping… CONTINUE READING
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