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Conference Paper: A Patch-Number and Bandwidth Adaptive Non-Local Kernel Regression Algorithm For Multiview Image Denoising

TitleA Patch-Number and Bandwidth Adaptive Non-Local Kernel Regression Algorithm For Multiview Image Denoising
Authors
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228
Citation
Proceedings of the 19th International Conference on Digital Signal Processing, Hong Kong, 20-23 Aug 2014 , p. 301-304 How to Cite?
AbstractThis paper presents an automatic patch number selection method for bandwidth adaptive non-local kernel regression (BA-NLKR) algorithm, which was recently proposed for improving the performance of conventional non-local kernel regression (NLKR) in image processing. Although BA-NLKR addressed the important issue of bandwidth selection, the number of non-local patches, which impacts the integration of local and non-local information, however is chosen empirically. In this paper, we propose a new algorithm for automatic patch number selection based on the intersecting confidence intervals (ICI) rule in order to achieve better performance. Moreover, the proposed patch number and bandwidth adaptive NLKR (PBA-NLKR) is applied to the denoising problem of multiview images. The effectiveness of the proposed algorithm is illustrated by experimental results on denoising for both single-view and multi-view images.
Persistent Identifierhttp://hdl.handle.net/10722/202943
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWu, JF-
dc.contributor.authorWang, C-
dc.contributor.authorLin, ZC-
dc.contributor.authorChan, SC-
dc.date.accessioned2014-09-19T10:10:51Z-
dc.date.available2014-09-19T10:10:51Z-
dc.date.issued2014-
dc.identifier.citationProceedings of the 19th International Conference on Digital Signal Processing, Hong Kong, 20-23 Aug 2014 , p. 301-304-
dc.identifier.isbn978-1-4799-4612-9-
dc.identifier.urihttp://hdl.handle.net/10722/202943-
dc.description.abstractThis paper presents an automatic patch number selection method for bandwidth adaptive non-local kernel regression (BA-NLKR) algorithm, which was recently proposed for improving the performance of conventional non-local kernel regression (NLKR) in image processing. Although BA-NLKR addressed the important issue of bandwidth selection, the number of non-local patches, which impacts the integration of local and non-local information, however is chosen empirically. In this paper, we propose a new algorithm for automatic patch number selection based on the intersecting confidence intervals (ICI) rule in order to achieve better performance. Moreover, the proposed patch number and bandwidth adaptive NLKR (PBA-NLKR) is applied to the denoising problem of multiview images. The effectiveness of the proposed algorithm is illustrated by experimental results on denoising for both single-view and multi-view images.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228-
dc.relation.ispartofProceedings of the International Conference on Digital Signal Processing-
dc.rightsProceedings of the International Conference on Digital Signal Processing. Copyright © IEEE.-
dc.rights©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleA Patch-Number and Bandwidth Adaptive Non-Local Kernel Regression Algorithm For Multiview Image Denoising-
dc.typeConference_Paper-
dc.identifier.emailWu, JF: jfwu@eee.hku.hk-
dc.identifier.emailWang, C: cwang@eee.hku.hk-
dc.identifier.emailLin, ZC: zclin@eee.hku.hk-
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hk-
dc.identifier.authorityChan, SC=rp00094en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDSP.2014.6900676-
dc.identifier.hkuros240656-
dc.identifier.spage301-
dc.identifier.epage304-
dc.publisher.placeUnited States-

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