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

Wenzhi Liao, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, Wilfried Philips, Peter Hobson (Editor), Gennaro Percannella (Editor), Mario Vento (Editor), Arnold Wiliem (Editor)

Research output: Contribution to conferencePaper

2 Citations (Scopus)

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.

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryUnited Kingdom
CityMelbourne, VIC
Period15/09/1318/09/13

Fingerprint

principal components analysis
principal component analysis
method

Keywords

  • total variation
  • classification
  • kernel principal component analysis
  • denoising
  • hyperspectral images
  • component analysi

Cite this

Liao, W., Aelterman, J., Luong, H., Pizurica, A., Philips, W., Hobson, P. (Ed.), ... Wiliem, A. (Ed.) (2013). Two-stage denoising method for hyperspectral images combining KPCA and total variation. 2048-2052. Paper presented at 2013 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne, VIC, United Kingdom. https://doi.org/10.1109/ICIP.2013.6738422
Liao, Wenzhi ; Aelterman, Jan ; Luong, Hiep ; Pizurica, Aleksandra ; Philips, Wilfried ; Hobson, Peter (Editor) ; Percannella, Gennaro (Editor) ; Vento, Mario (Editor) ; Wiliem, Arnold (Editor). / Two-stage denoising method for hyperspectral images combining KPCA and total variation. Paper presented at 2013 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne, VIC, United Kingdom.5 p.
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title = "Two-stage denoising method for hyperspectral images combining KPCA and total variation",
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 analysi",
author = "Wenzhi Liao and Jan Aelterman and Hiep Luong and Aleksandra Pizurica and Wilfried Philips and Peter Hobson and Gennaro Percannella and Mario Vento and Arnold Wiliem",
year = "2013",
doi = "10.1109/ICIP.2013.6738422",
language = "English",
pages = "2048--2052",
note = "2013 20th IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",

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Liao, W, Aelterman, J, Luong, H, Pizurica, A, Philips, W, Hobson, P (ed.), Percannella, G (ed.), Vento, M (ed.) & Wiliem, A (ed.) 2013, 'Two-stage denoising method for hyperspectral images combining KPCA and total variation' Paper presented at 2013 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne, VIC, United Kingdom, 15/09/13 - 18/09/13, pp. 2048-2052. https://doi.org/10.1109/ICIP.2013.6738422

Two-stage denoising method for hyperspectral images combining KPCA and total variation. / Liao, Wenzhi; Aelterman, Jan; Luong, Hiep; Pizurica, Aleksandra; Philips, Wilfried; Hobson, Peter (Editor); Percannella, Gennaro (Editor); Vento, Mario (Editor); Wiliem, Arnold (Editor).

2013. 2048-2052 Paper presented at 2013 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne, VIC, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Two-stage denoising method for hyperspectral images combining KPCA and total variation

AU - Liao, Wenzhi

AU - Aelterman, Jan

AU - Luong, Hiep

AU - Pizurica, Aleksandra

AU - Philips, Wilfried

A2 - Hobson, Peter

A2 - Percannella, Gennaro

A2 - Vento, Mario

A2 - Wiliem, Arnold

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - total variation

KW - classification

KW - kernel principal component analysis

KW - denoising

KW - hyperspectral images

KW - component analysi

UR - http://hdl.handle.net/1854/LU-4143226

U2 - 10.1109/ICIP.2013.6738422

DO - 10.1109/ICIP.2013.6738422

M3 - Paper

SP - 2048

EP - 2052

ER -

Liao W, Aelterman J, Luong H, Pizurica A, Philips W, Hobson P, (ed.) et al. Two-stage denoising method for hyperspectral images combining KPCA and total variation. 2013. Paper presented at 2013 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne, VIC, United Kingdom. https://doi.org/10.1109/ICIP.2013.6738422