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.
Original languageEnglish
Pages2048-2052
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, United Kingdom
Duration: 15 Sep 201318 Sep 2013

Conference

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

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.), Percannella, G. (Ed.), Vento, M. (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