A Nonlinear Stein Based Estimator for Multichannel Image Denoising

By Chaux, Duval, Benazza-Benyahia, Pesquet

Abstract:

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.

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