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18th International Conference on Pattern Recognition (ICPR'06) Volume 3
Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
Paul Scheunders, University of Antwerp, Universiteitsplein, Belgium
Steve De Backer, University of Antwerp, Universiteitsplein, Belgium
In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial waveletbased denoising techniques, based on Bayesian leastsquares optimization procedures, using a prior model for the wavelet coefficients that account for the intercorrelations between the multicomponent bands.The applied prior model for the multicomponent signal is a Gaussian Scale Mixture (GSM) model. The method is compared to single-band wavelet denoising and to multiband denoising using a Gaussian prior. Experiments on a Landsat multispectral remote sensing image are conducted.
Citation:
Paul Scheunders, Steve De Backer, "Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model," icpr, vol. 3, pp.754-757, 18th International Conference on Pattern Recognition (ICPR'06) Volume 3, 2006
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