Improved Wavelet Denoising via Empirical Wiener Filtering

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameSPIE Technical Conference on Wavelet Applications in Signal Processingen_US
dc.citation.locationSan Diego, CAen_US
dc.contributor.authorGhael, Sadeepen_US
dc.contributor.authorSayeed, Akbar M.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:44:26Z
dc.date.available2007-10-31T00:44:26Z
dc.date.issued1997-07-01en
dc.date.modified2006-07-05en_US
dc.date.note2004-01-08en_US
dc.date.submitted1997-07-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractWavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. Wavelet shrinkage using thresholding is asymptotically optimal in a minimax mean-square error (MSE) sense over a variety of smoothness spaces. However, for any given signal, the MSE-optimal processing is achieved by the Wiener filter, which delivers substantially improved performance. In this paper, we develop a new algorithm for wavelet denoising that uses a wavelet shrinkage estimate as a means to design a wavelet-domain Wiener filter. The shrinkage estimate indirectly yields an estimate of the signal subspace that is leveraged into the design of the filter. A peculiar aspect of the algorithm is its use of two wavelet bases: one for the design of the empirical Wiener filter and one for its application. Simulation results show up to a factor of 2 improvement in MSE over wavelet shrinkage, with a corresponding improvement in visual quality of the estimate. Simulations also yield a remarkable observation: whereas shrinkage estimates typically improve performance by trading bias for variance or vice versa, the proposed scheme typically decreases both bias and variance compared to wavelet shrinkage.en_US
dc.identifier.citationS. Ghael, A. M. Sayeed and R. G. Baraniuk, "Improved Wavelet Denoising via Empirical Wiener Filtering," 1997.
dc.identifier.doihttp://dx.doi.org/10.1117/12.292799en_US
dc.identifier.urihttps://hdl.handle.net/1911/19895
dc.language.isoeng
dc.subjectwavelets*
dc.subjectdenoising*
dc.subjectestimation*
dc.subjectWiener filter*
dc.subjectsubspace*
dc.subject.keywordwaveletsen_US
dc.subject.keyworddenoisingen_US
dc.subject.keywordestimationen_US
dc.subject.keywordWiener filteren_US
dc.subject.keywordsubspaceen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.titleImproved Wavelet Denoising via Empirical Wiener Filteringen_US
dc.typeConference paper
dc.type.dcmiText
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