A block-thresholding method for multispectral image denoising

Caroline Chaux; Amel Benazza-Benyahia; Jean-Christophe Pesquet

doi:10.1117/12.617880

17 September 2005 A block-thresholding method for multispectral image denoising

Caroline Chaux, Amel Benazza-Benyahia, Jean-Christophe Pesquet

Proceedings Volume 5914, Wavelets XI; 59141H (2005) https://doi.org/10.1117/12.617880
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

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.

Citation Download Citation

Caroline Chaux, Amel Benazza-Benyahia, and Jean-Christophe Pesquet "A block-thresholding method for multispectral image denoising", Proc. SPIE 5914, Wavelets XI, 59141H (17 September 2005); https://doi.org/10.1117/12.617880

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