Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1253
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dc.contributorDepartment of Computing-
dc.creatorZhang, L-
dc.creatorBao, P-
dc.creatorZhang, DD-
dc.date.accessioned2014-12-11T08:23:55Z-
dc.date.available2014-12-11T08:23:55Z-
dc.identifier.isbn0-7803-7663-3-
dc.identifier.urihttp://hdl.handle.net/10397/1253-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectCorrelation methodsen_US
dc.subjectSpurious signal noiseen_US
dc.subjectStatistical methodsen_US
dc.subjectVectorsen_US
dc.subjectWavelet transformsen_US
dc.titleInterscale image denoising with wavelet context modelingen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents a wavelet-based linear minimum mean square-error estimation (LMMSE) scheme to exploit the strong wavelet interscale dependencies for image denoising. Using overcomplete wavelet expansion (OWE), we group the wavelet coefficients with the same spatial orientation at adjacent scales as a vector. The LMMSE algorithm is then applied to the vector variable. This scheme exploits the correlation information of wavelet scales to improve noise removal. To calculate the statistics of wavelet coefficients more adaptively, we classify them into different clusters by the context modeling technique, which yields a good local discrimination between edge structures and backgrounds. Experiments show that the proposed scheme outperforms some existing denoising methods. And a biorthogonal wavelet, which well characterizes the interscale dependencies, is found very suitable for the scheme.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2003 IEEE International Conference on Acoustics, Speech, and Signal Processing : April 6-10, 2003, Hong Kong, v. 6, p. 97-100-
dcterms.issued2003-
dc.identifier.isiWOS:000185463700025-
dc.identifier.scopus2-s2.0-0141631423-
dc.identifier.rosgroupidr11472-
dc.description.ros2002-2003 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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