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Gamma-Minimax Wavelet Shrinkage: A Robust Incorporation of Information about Energy of a Signal in Denoising Applications (Articolo in rivista)
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- Label
- Gamma-Minimax Wavelet Shrinkage: A Robust Incorporation of Information about Energy of a Signal in Denoising Applications (Articolo in rivista) (literal)
- Anno
- 2004-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Angelini C.; Vidakovic B. (literal)
- Pagina inizio
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www3.stat.sinica.edu.tw/statistica/oldpdf/A14n14.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- Scopu (literal)
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto per le Applicazioni del Calcolo -Sezione di Napoli and Georgia Institute of Technology (literal)
- Titolo
- Gamma-Minimax Wavelet Shrinkage: A Robust Incorporation of Information about Energy of a Signal in Denoising Applications (literal)
- Abstract
- In this paper we propose a method for wavelet filtering of noisy signals
when prior information about the L2 energy of the signal of interest is available?
Assuming the independence model? according to which the wavelet coe?cients are
treated individually? we propose a level dependent shrinkage rule that turns out to
be the ??minimax rule for a suitable class? say ?? of realistic priors on the wavelet
coe?cients?
The proposed methodology is particularly well suited for denoising tasks where
signal?to?noise ratio is low? and it is illustrated on a battery of standard test function
tions? Performance comparisons with some others methods existing in the literature
are provided? An example in atomic force microscopy ?AFM? is also discussed?
Key words and phrases? Atomic force microscopy? bounded normal mean? ??mini?
maxity? shrinkage? wavelet regression? (literal)
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