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dc.contributor.authorGhael, Sadeep
Sayeed, Akbar M.
Baraniuk, Richard G.
dc.creatorGhael, Sadeep
Sayeed, Akbar M.
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T00:44:26Z
dc.date.available 2007-10-31T00:44:26Z
dc.date.issued 1997-07-01
dc.date.submitted 1997-07-01
dc.identifier.citation S. Ghael, A. M. Sayeed and R. G. Baraniuk, "Improved Wavelet Denoising via Empirical Wiener Filtering," 1997.
dc.identifier.urihttps://hdl.handle.net/1911/19895
dc.description Conference Paper
dc.description.abstract Wavelet 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.
dc.language.iso eng
dc.subjectwavelets
denoising
estimation
Wiener filter
subspace
dc.subject.otherWavelet based Signal/Image Processing
dc.title Improved Wavelet Denoising via Empirical Wiener Filtering
dc.type Conference paper
dc.date.note 2004-01-08
dc.citation.bibtexName inproceedings
dc.date.modified 2006-07-05
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordwavelets
denoising
estimation
Wiener filter
subspace
dc.citation.location San Diego, CA
dc.citation.conferenceName SPIE Technical Conference on Wavelet Applications in Signal Processing
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1117/12.292799


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1426]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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