loading...
Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model
Hong Kong August 20-August 24
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.118518th International Conference on Patt ...
 This Article 
 
PDF
HTML
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
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
Usage of this product signifies your acceptance of the Terms of Use.