Noise reduction of hyperspectral imagery using nonlocal sparse representation with spectral-spatial structure

@article{Qian2012NoiseRO,
  title={Noise reduction of hyperspectral imagery using nonlocal sparse representation with spectral-spatial structure},
  author={Yuntao Qian and Minchao Ye and Qi Wang},
  journal={2012 IEEE International Geoscience and Remote Sensing Symposium},
  year={2012},
  pages={3467-3470}
}
Noise reduction is always an active research area in image processing due to its importance for the sequential tasks such as object classification and detection. In this paper, we develop a sparse representation based noise reduction method for hyperspectral imagery, which is dependent on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while noise component has not this property. The main contribution of the paper is… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-8 of 8 references

Denoising and dimensionality re­ duction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis

  • G. Chen, S. Qian
  • International Journal of Remote Sensing, vol. 30…
  • 2009
3 Excerpts

A review of image denoising algorithms, with a new one

  • A. Buades, B. Coli, J. M. Morel
  • Multisc. Model. Simulat., vol. 4, no. 2, pp. 490…
  • 2005
2 Excerpts

Similar Papers

Loading similar papers…