An interscale multivariate statistical model for MAP multicomponent image denoising in the wavelet transform domain
A. Elmzoughi, Amel Benazza‐Benyahia, Jean‐Christophe Pesquet · 2006
The paper presents the design of a multivariate statistical approach for multicomponent image denoising in the wavelet transform domain. We extend an approach that we have recently proposed, where 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 multivariate Bernoulli-Gaussian distribution is used as a prior model. The paper develops low-complexity maximum a posteriori rules that exploit jointly the intra- and interscale redundancies between the wavelet coefficients. Experimental results carried out on remote sensing multispectral images show that the proposed procedure improves the state-of-the-art wavelet-based denoising methods.