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Two-stage denoising method for hyperspectral images combining KPCA and total variation

Wenzhi Liao (UGent) , Jan Aelterman (UGent) , Hiep Luong (UGent) , Aleksandra Pizurica (UGent) and Wilfried Philips (UGent)
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Project
SBO-IWT project Chameleon: Domainspecific Hyperspectral Imaging Systems for Relevant Industrial Applications
Abstract
This paper presents a two-stage denoising method for hyperspectral image (HSI) by combining kernel principal component analysis (KPCA) and total variation (TV). In the first stage, we use KPCA denoising to reduce spectrally uncorrelated noise. In the second stage, the information content is largely separated from the remaining noise by means of principal component analysis (PCA). The remaining noise is then efficiently removed by fast primal-dual TV denoising in lowenergy PCA channels. Experimental results on simulated and real HSIs are very encouraging.
Keywords
total variation, classification, kernel principal component analysis, denoising, Hyperspectral images, COMPONENT ANALYSIS

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Citation

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MLA
Liao, Wenzhi et al. “Two-stage Denoising Method for Hyperspectral Images Combining KPCA and Total Variation.” IEEE International Conference on Image Processing ICIP. Ed. Peter Hobson et al. IEEE, 2013. 2048–2052. Print.
APA
Liao, W., Aelterman, J., Luong, H., Pizurica, A., & Philips, W. (2013). Two-stage denoising method for hyperspectral images combining KPCA and total variation. In P. Hobson, G. Percannella, M. Vento , & A. Wiliem (Eds.), IEEE International Conference on Image Processing ICIP (pp. 2048–2052). Presented at the IEEE International Conference on Image Processing (ICIP - 2013), IEEE.
Chicago author-date
Liao, Wenzhi, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, and Wilfried Philips. 2013. “Two-stage Denoising Method for Hyperspectral Images Combining KPCA and Total Variation.” In IEEE International Conference on Image Processing ICIP, ed. Peter Hobson, Gennaro Percannella, Mario Vento , and Arnold Wiliem, 2048–2052. IEEE.
Chicago author-date (all authors)
Liao, Wenzhi, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, and Wilfried Philips. 2013. “Two-stage Denoising Method for Hyperspectral Images Combining KPCA and Total Variation.” In IEEE International Conference on Image Processing ICIP, ed. Peter Hobson, Gennaro Percannella, Mario Vento , and Arnold Wiliem, 2048–2052. IEEE.
Vancouver
1.
Liao W, Aelterman J, Luong H, Pizurica A, Philips W. Two-stage denoising method for hyperspectral images combining KPCA and total variation. In: Hobson P, Percannella G, Vento M, Wiliem A, editors. IEEE International Conference on Image Processing ICIP. IEEE; 2013. p. 2048–52.
IEEE
[1]
W. Liao, J. Aelterman, H. Luong, A. Pizurica, and W. Philips, “Two-stage denoising method for hyperspectral images combining KPCA and total variation,” in IEEE International Conference on Image Processing ICIP, Melbourne, Australia, 2013, pp. 2048–2052.
@inproceedings{4143226,
  abstract     = {This paper presents a two-stage denoising method for hyperspectral image (HSI) by combining kernel principal component analysis (KPCA) and total variation (TV). In the first stage, we use KPCA denoising to reduce spectrally uncorrelated noise. In the second stage, the information content is largely separated from the remaining noise by means of principal component analysis (PCA). The remaining noise is then efficiently removed by fast primal-dual TV denoising in lowenergy PCA channels. Experimental results on simulated and real HSIs are very encouraging.},
  author       = {Liao, Wenzhi and Aelterman, Jan and Luong, Hiep and Pizurica, Aleksandra and Philips, Wilfried},
  booktitle    = {IEEE International Conference on Image Processing ICIP},
  editor       = {Hobson, Peter and Percannella, Gennaro  and Vento , Mario  and Wiliem, Arnold },
  isbn         = {9781479923403},
  keywords     = {total variation,classification,kernel principal component analysis,denoising,Hyperspectral images,COMPONENT ANALYSIS},
  language     = {eng},
  location     = {Melbourne, Australia},
  pages        = {2048--2052},
  publisher    = {IEEE},
  title        = {Two-stage denoising method for hyperspectral images combining KPCA and total variation},
  url          = {http://www.ieeeicip.org/default.asp},
  year         = {2013},
}

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