Noise reduction for enhanced component identification in multi-dimensional biomolecular NMR studies
Computational Statistics & Data Analysis
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
Asymptotic mean and variance of Gini correlation for bivariate normal samples
IEEE Transactions on Signal Processing
Nonparametric cepstrum estimation via optimal risk smoothing
IEEE Transactions on Signal Processing
Least squares estimation without priors or supervision
Neural Computation
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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 intercomponent 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.