By Topic

Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Scheunders, P. ; Dept. of Phys., Antwerp Univ. ; De Backer, S.

In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using a prior model for the wavelet coefficients that account for the inter-correlations 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 Land-sat multispectral remote sensing image are conducted

Published in:

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:3 )

Date of Conference:

0-0 0