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dc.contributor.authorVelisavljević, Vladanen_GB
dc.contributor.authorDragotti, Pier Luigien_GB
dc.contributor.authorVetterli, Martinen_GB
dc.date.accessioned2013-05-31T10:30:51Z
dc.date.available2013-05-31T10:30:51Z
dc.date.issued2002
dc.identifier.citationVelisavljevic, V.; Dragotti, P. and Vetterli, M. (2002) 'Directional wavelet transforms and frames', International Conference on Image Processing, Rochester, NY, USA, 24-28 June 2002. Rochester: IEEE, vol.3, pp.589-592en_GB
dc.identifier.isbn0780376226
dc.identifier.doi10.1109/ICIP.2002.1039039
dc.identifier.urihttp://hdl.handle.net/10547/293130
dc.description.abstractThe application of the wavelet transform in image processing is most frequently based on a separable transform. Lines and columns in an image are treated independently and the basis functions are simply products of corresponding one-dimensional functions. Such a method keeps simplicity in design and computation. A new two-dimensional approach is proposed, which retains the simplicity of separable processing, but allows more directionalities. The method can be applied in many areas like denoising, nonlinear approximation and compression. The results on nonlinear approximation and denoising show interesting gains compared to the standard two-dimensional analysis.
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1039039en_GB
dc.subjectdata compressionen_GB
dc.subjectimage codingen_GB
dc.subjectwavelet transformsen_GB
dc.titleDirectional wavelet transforms and framesen
dc.typeConference papers, meetings and proceedingsen
html.description.abstractThe application of the wavelet transform in image processing is most frequently based on a separable transform. Lines and columns in an image are treated independently and the basis functions are simply products of corresponding one-dimensional functions. Such a method keeps simplicity in design and computation. A new two-dimensional approach is proposed, which retains the simplicity of separable processing, but allows more directionalities. The method can be applied in many areas like denoising, nonlinear approximation and compression. The results on nonlinear approximation and denoising show interesting gains compared to the standard two-dimensional analysis.


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