AustralieFrV1, PascalFrancis, Curation, bibRecord, 002482

Wavelet deconvolution with noisy eigenvalues

Identifieur interne : 002482 ( PascalFrancis/Curation ); précédent : 002481; suivant : 002483

Wavelet deconvolution with noisy eigenvalues

Auteurs : Laurent Cavalier [France] ; Marc Raimondo [Australie]

Source :

IEEE transactions on signal processing [ 1053-587X ] ; 2007.

RBID : Pascal:07-0268082

Descripteurs français

Pascal (Inist)
- Déconvolution, Valeur propre, Transformation ondelette, Reconstruction signal, Restauration signal, Image bruitée, Image floue, Evaluation performance, Approximation linéaire, Restauration image, Convolution, Signal entrée, Système linéaire, Estimation adaptative, Décomposition valeur singulière, Distribution Wigner Ville, Traitement signal, Traitement image.

English descriptors

KwdEn :
- Adaptive estimation, Blurred image, Convolution, Deconvolution, Eigenvalue, Image processing, Image restoration, Input signal, Linear approximation, Linear system, Noisy image, Performance evaluation, Signal processing, Signal reconstruction, Signal restoration, Singular value decomposition, Wavelet transformation, Wigner Ville distribution.

Abstract

Over the last decade, there has been a lot of interest in wavelet-vaguelette methods for the recovery of noisy signals or images in motion blur. Nonlinear wavelet estimators are known to have good adaptive properties and to outperform linear approximations over a wide range of signals and images, see e.g., the recent WaveD method of Johnstone, Kerkyacharian, Picard, and Raimondo (2004) or the ForWarD method of Neelamani, Choi, and Baraniuk (2004) and also Fan and Koo (2002) in the density setting. In the deblurring setting, wavelet-vaguelette methods rely on the complete knowledge of a convolution operator's eigenvalues. This is an unlikely situation in practice, however. A more realistic scenario, such as would arise when passing the Fourier basis as an input signal through a linear-time-invariant system, is to imagine that one also observes a set of noisy eigenvalues. In this paper, we define a version of the WaveD estimator which is near-optimal when used with noisy eigenvalues. A key feature of our method includes a data-driven method for choosing the fine resolution level in WaveD estimation. Asymptotic theory is illustrated with a wide range of finite sample examples.

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Le document en format XML

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<fC03 i1="16" i2="X" l="FRE"><s0>Distribution Wigner Ville</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Wigner Ville distribution</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Distribución Wigner Ville</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Traitement signal</s0>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Signal processing</s0>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Procesamiento señal</s0>
<s5>31</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Traitement image</s0>
<s5>32</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Image processing</s0>
<s5>32</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Procesamiento imagen</s0>
<s5>32</s5>
</fC03>
<fN21><s1>176</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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   |texte=   Wavelet deconvolution with noisy eigenvalues
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	Serveur d'exploration sur les relations entre la France et l'Australie
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Serveur d'exploration sur les relations entre la France et l'Australie

Wavelet deconvolution with noisy eigenvalues

Wavelet deconvolution with noisy eigenvalues

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