Title:
Γ -Minimax Wavelet Shrinkage: A Robust Incorporation of Information about Energy of a Signal in Denoising Applications

dc.contributor.author Angelini, Claudia
dc.contributor.author Vidakovic, Brani
dc.contributor.corporatename Georgia Institute of Technology
dc.contributor.corporatename Consiglio nazionale delle ricerche (Italy). Istituto per Applicazioni della Matematica
dc.date.accessioned 2008-12-10T15:52:13Z
dc.date.available 2008-12-10T15:52:13Z
dc.date.issued 2001
dc.description.abstract In this paper we propose a method for wavelet- filtering of noisy signals when prior information about the energy of the signal of interest is available. Assuming the independence model, according to which the wavelet coefficients are treated individually, we propose a level dependent shrinkage rule that turns out to be the Γ-minimax rule for a suitable class Γ of realistic priors on the wavelet coefficients. The proposed methodology, particularly applicable to noisy signals with a low signal to noise ratio, is illustrated on a battery of standard test functions. A real-life example in atomic force microscopy (AFM) is also discussed. en
dc.identifier.uri http://hdl.handle.net/1853/25933
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries Biomedical Engineering Technical Report ; G06/2001 en
dc.subject Wavelet regression en
dc.subject Shrinkage en
dc.subject Bounded normal mean en
dc.subject Γ -minimaxity en
dc.subject Atomic force microscopy en
dc.title Γ -Minimax Wavelet Shrinkage: A Robust Incorporation of Information about Energy of a Signal in Denoising Applications en
dc.title.alternative Γ-Minimax Wavelet Shrinkage en
dc.type Text
dc.type.genre Technical Report
dspace.entity.type Publication
local.contributor.author Vidakovic, Brani
local.contributor.corporatename Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename College of Engineering
relation.isAuthorOfPublication 1463fd97-3d52-4269-afac-97f6f7f46fcd
relation.isOrgUnitOfPublication da59be3c-3d0a-41da-91b9-ebe2ecc83b66
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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