An extended sure approach for multicomponent image denoising

Multichannel imaging systems provide several observations of the same scene which are often corrupted by additive noise. We are interested in multispectral image denoising in the wavelet domain. We adopt a multivariate approach in order to exploit the correlations existing between the different spectral components. Our main contribution is the application of Stein´s principle to build a new estimator for arbitrary multichannel images embedded in Gaussian noise. Simulation tests carried out on multispectral satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques.