Hyperspectral Imagery Restoration Using Nonlocal Spectral-Spatial Structured Sparse Representation With Noise Estimation

@article{Qian2013HyperspectralIR,
  title={Hyperspectral Imagery Restoration Using Nonlocal Spectral-Spatial Structured Sparse Representation With Noise Estimation},
  author={Yuntao Qian and Minchao Ye},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2013},
  volume={6},
  pages={499-515}
}
Noise reduction is an active research area in image processing due to its importance in improving the quality of image for object detection and classification. 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 an observed signal can be sparsely decomposed over a redundant dictionary while the noise component does not have this property. The main contribution of the paper… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 85 CITATIONS, ESTIMATED 39% COVERAGE

FILTER CITATIONS BY YEAR

2013
2019

CITATION STATISTICS

  • 5 Highly Influenced Citations

  • Averaged 18 Citations per year over the last 3 years

  • 47% Increase in citations per year in 2018 over 2017

References

Publications referenced by this paper.
SHOWING 1-10 OF 53 REFERENCES

Similar Papers

Loading similar papers…