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- aggregation classification "A1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 1002600.bibtex.
- aggregation hasFormat 1002600.csv.
- aggregation hasFormat 1002600.dc.
- aggregation hasFormat 1002600.didl.
- aggregation hasFormat 1002600.doc.
- aggregation hasFormat 1002600.json.
- aggregation hasFormat 1002600.mets.
- aggregation hasFormat 1002600.mods.
- aggregation hasFormat 1002600.rdf.
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- aggregation hasFormat 1002600.txt.
- aggregation hasFormat 1002600.xls.
- aggregation hasFormat 1002600.yaml.
- aggregation isPartOf urn:issn:1053-587X.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "A recursive scheme for computing autocorrelation functions of decimated complex wavelet subbands".
- aggregation abstract "This paper deals with the problem of the exact computation of the autocorrelation function of a real or complex discrete wavelet subband of a signal, when the autocorrelation function (or Power Spectral Density, PSD) of the signal in the time domain (or spatial domain) is either known or estimated using a separate technique. The solution to this problem allows us to couple time domain noise estimation techniques to wavelet domain denoising algorithms, which is crucial for the development of blind wavelet-based denoising techniques. Specifically, we investigate the Dual-Tree complex wavelet transform (DT-CWT), which has a good directional selectivity in 2-D and 3-D, is approximately shift-invariant, and yields better denoising results than a discrete wavelet transform (DWT). The proposed scheme gives an analytical relationship between the PSD of the input signal/image and the PSD of each individual real/complex wavelet subband which is very useful for future developments. We also show that a more general technique, that relies on Monte-Carlo simulations, requires a large number of input samples for a reliable estimate, while the proposed technique does not suffer from this problem.".
- aggregation authorList BK673980.
- aggregation endPage "3912".
- aggregation issue "7".
- aggregation startPage "3907".
- aggregation volume "58".
- aggregation aggregates 1002653.
- aggregation isDescribedBy 1002600.
- aggregation similarTo TSP.2010.2047392.
- aggregation similarTo LU-1002600.