  The use of multicomponent images has become widespread with the improvement
of multisensor systems having increased spatial and spectral resolutions.
However, the observed images are often corrupted by an additive Gaussian noise.
In this paper, we are interested in multichannel image denoising based on a
multiscale representation of the images. A multivariate statistical approach is
adopted to take into account both the spatial and the inter-component
correlations existing between the different wavelet subbands. More precisely,
we propose a new parametric nonlinear estimator which generalizes many reported
denoising methods. The derivation of the optimal parameters is achieved by
applying Stein's principle in the multivariate case. Experiments performed on
multispectral remote sensing images clearly indicate that our method
outperforms conventional wavelet denoising techniques
