The objective of this paper is to design a new estimator for multicomponent image denoising in the wavelet transform domain. To this end, we extend the block-based thresholding method initially proposed by Cai and Silverman, which takes advantage of the spatial dependence between the wavelet coefficients. In the case of multispectral images, we develop a more general framework for block-based shrinkage, the blocks being built from various combinations of the wavelet coefficients of the different image channels at adjacent spatial positions, for a given orientation and resolution level. In the presence of possibly spectrally correlated Gaussian noise, the parameters of the resulting estimator are optimized from the data by exploiting Stein's principle. Simulations show the higher performance of our estimator for denoising multispectral satellite images.

Date Published: 17 September 2005
PDF: 13 pages
Proc. SPIE 5914, Wavelets XI, 59141H (17 September 2005); doi: 10.1117/12.617880

Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)