A nonlinear Stein based estimator for multichannel image denoising - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Signal Processing Year : 2008

A nonlinear Stein based estimator for multichannel image denoising


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
Fichier principal
Vignette du fichier
Chaux_C_2008_j-ieee-tsp_nonlinear_sbemid.pdf (289 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00617318 , version 1 (11-06-2016)



Caroline Chaux, Laurent Duval, Amel Benazza-Benyahia, Jean-Christophe Pesquet. A nonlinear Stein based estimator for multichannel image denoising. IEEE Transactions on Signal Processing, 2008, 56 (8), pp.3855-3870. ⟨10.1109/TSP.2008.921757⟩. ⟨hal-00617318⟩
235 View
91 Download



Gmail Facebook Twitter LinkedIn More