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This paper introduces an image denoising procedure based on a 2D scale-mixing complex-valued wavelet transform. Both the minimal (unitary) and redundant (maximum overlap) versions of the transform are used. The covariance structure of white noise in wavelet domain is established. Estimation is performed via empirical Bayesian techniques, including versions(More)
—We apply a non-parametric regression technique based on second generation wavelets to irregularly spaced network data. Conventional wavelet based non-parametric regression can be modelled as, f i = gi +i, where fi = f (ti), gi = g(ti) and i = 1, ..., n. Key requirements for this model are that n = 2 J for some J ∈ N, data are observed on a regular grid ti(More)
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