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Article: On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction

TitleOn Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction
Authors
KeywordsAdaptive kernel selection
Bias-variance tradeoff
Image processing
Kernel regression
Multidimensional signal processing
Multivariate local polynomial regression
Issue Date2011
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/
Citation
Journal of Signal Processing Systems, 2011, v. 64 n. 3, p. 361-374 How to Cite?
AbstractThis paper studies the problem of adaptive kernel selection for multivariate local polynomial regression (LPR) and its application to smoothing and reconstruction of noisy images. In multivariate LPR, the multidimensional signals are modeled locally by a polynomial using least-squares (LS) criterion with a kernel controlled by a certain bandwidth matrix. Based on the traditional intersection confidence intervals (ICI) method, a new refined ICI (RICI) adaptive scale selector for symmetric kernel is developed to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of multidimensional signals. The resulting multivariate LPR method called the steering-kernel-based LPR with refined ICI method (SK-LPR-RICI) is applied to the smoothing and reconstruction problems in noisy images. Simulation results show that the proposed SK-LPR-RICI method has a better PSNR and visual performance than conventional LPR-based methods in image processing. © 2010 The Author(s).
Persistent Identifierhttp://hdl.handle.net/10722/124715
ISSN
2015 Impact Factor: 0.508
2015 SCImago Journal Rankings: 0.262
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grant Council (RGC)
Funding Information:

This work was supported in part by the Hong Kong Research Grant Council (RGC). Parts of this work were presented at the Conference of 2009 IEEE International Symposium on Circuits and Systems [32].

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChan, SCen_HK
dc.date.accessioned2010-10-31T10:50:03Z-
dc.date.available2010-10-31T10:50:03Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal of Signal Processing Systems, 2011, v. 64 n. 3, p. 361-374en_HK
dc.identifier.issn1939-8018en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124715-
dc.description.abstractThis paper studies the problem of adaptive kernel selection for multivariate local polynomial regression (LPR) and its application to smoothing and reconstruction of noisy images. In multivariate LPR, the multidimensional signals are modeled locally by a polynomial using least-squares (LS) criterion with a kernel controlled by a certain bandwidth matrix. Based on the traditional intersection confidence intervals (ICI) method, a new refined ICI (RICI) adaptive scale selector for symmetric kernel is developed to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of multidimensional signals. The resulting multivariate LPR method called the steering-kernel-based LPR with refined ICI method (SK-LPR-RICI) is applied to the smoothing and reconstruction problems in noisy images. Simulation results show that the proposed SK-LPR-RICI method has a better PSNR and visual performance than conventional LPR-based methods in image processing. © 2010 The Author(s).en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/en_HK
dc.relation.ispartofJournal of Signal Processing Systemsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe original publication is available at www.springerlink.com-
dc.rightsThe Author(s)-
dc.subjectAdaptive kernel selectionen_HK
dc.subjectBias-variance tradeoffen_HK
dc.subjectImage processingen_HK
dc.subjectKernel regressionen_HK
dc.subjectMultidimensional signal processingen_HK
dc.subjectMultivariate local polynomial regressionen_HK
dc.titleOn Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstructionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1939-8018&volume=&spage=1&epage=14&date=2010&atitle=On+kernel+selection+of+multivariate+local+polynomial+modelling+and+its+application+to+image+smoothing+and+reconstructionen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s11265-010-0495-4en_HK
dc.identifier.scopuseid_2-s2.0-84883416967en_HK
dc.identifier.hkuros174333en_HK
dc.identifier.volume64-
dc.identifier.issue3-
dc.identifier.spage361en_HK
dc.identifier.epage374en_HK
dc.identifier.isiWOS:000293711100007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhang, ZG=8407277900en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.citeulike7376725-

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