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Please use this identifier to cite or link to this item: https://doi.org/10.1109/83.951535
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dc.titleLagrange wavelets for signal processing
dc.contributor.authorShi, Z.
dc.contributor.authorWei, G.W.
dc.contributor.authorKouri, D.J.
dc.contributor.authorHoffman, D.K.
dc.contributor.authorBao, Z.
dc.date.accessioned2014-10-28T03:12:06Z
dc.date.available2014-10-28T03:12:06Z
dc.date.issued2001-10
dc.identifier.citationShi, Z., Wei, G.W., Kouri, D.J., Hoffman, D.K., Bao, Z. (2001-10). Lagrange wavelets for signal processing. IEEE Transactions on Image Processing 10 (10) : 1488-1508. ScholarBank@NUS Repository. https://doi.org/10.1109/83.951535
dc.identifier.issn10577149
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104808
dc.description.abstractThis paper deals with the design of interpolating wavelets based on a variety of Lagrange functions, combined with novel signal processing techniques for digital imaging. Halfband Lagrange wavelets, B-spline Lagrange wavelets and Gaussian Lagrange [Lagrange distributed approximating functional (DAF)] wavelets are presented as specific examples of the generalized Lagrange wavelets. Our approach combines the perceptually dependent visual group normalization (VGN) technique and a softer logic masking (SLM) method. These are utilized to rescale the wavelet coefficients, remove perceptual redundancy and obtain good visual performance for digital image processing.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/83.951535
dc.sourceScopus
dc.subjectDistributed approximating functionals
dc.subjectGeneralized Lagrange wavelets
dc.subjectSofter logic masking
dc.subjectVisual group normalization
dc.typeArticle
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.description.doi10.1109/83.951535
dc.description.sourcetitleIEEE Transactions on Image Processing
dc.description.volume10
dc.description.issue10
dc.description.page1488-1508
dc.description.codenIIPRE
dc.identifier.isiut000171135800009
Appears in Collections:Staff Publications

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