AN INTERSCALE MULTIVARIATE MAP ESTIMATION OF MULTISPECTRAL IMAGES (WedAmOR3)

Author(s) : Amel Benazza-Benyahia (Ecole Supérieure des Communications de Tunis,, Tunisia) Jean-Christophe Pesquet (IGM and UMR-CNRS 5141, France)

Abstract : In this paper, we develop a multivariate statistical approach for image denoising in the wavelet transformed domain. To this respect, the wavelet coefficients of all the image channels at the same spatial position, in a given orientation and at the same resolution level, are grouped into a vector and a maximum a posteriori estimate is derived from a multivariate Bernouilli-Gaussian prior. The parameters of this statistical model are computed recursively from coarse to fine resolutions in order to exploit the inter-scale redundancies between the wavelet coefficients. Simulation tests performed on remote sensing multispectral images indicate that the proposed procedure improves the conventional wavelet-based denoising methods.

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