30 October 1997 Nonlinear shrinkage estimation with complex Daubechies wavelets
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One of the main advantages of the discrete wavelet representation is the near-optimal estimation of signals corrupted with noise. After the seminal work of De Vore and Lucier (1992) and Donoho and Johnstone (1995), new techniques for choosing appropriate threshold and/or shrinkage functions have recently been explored by Bayesian and likelihood methods. This work is motivated by a Bayesian approach and is based on the complex representation of signals by the Symmetric Daubechies Wavelets. Applications for two dimensional signals are discussed.
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Jean-Marc Lina, Jean-Marc Lina, Brenda MacGibbon, Brenda MacGibbon, } "Nonlinear shrinkage estimation with complex Daubechies wavelets", Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); doi: 10.1117/12.279680; https://doi.org/10.1117/12.279680

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