Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/11582
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dc.contributor.authorRamya, C-
dc.contributor.authorRani, S Subha-
dc.date.accessioned2011-04-21T06:45:29Z-
dc.date.available2011-04-21T06:45:29Z-
dc.date.issued2011-04-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/11582-
dc.description251-255en_US
dc.description.abstractThis study presents a novel dictionary pruning algorithm that found dictionaries of optimized size for a given dataset, without compromising its approximation accuracy and performance. It is achieved by applying KSVD (K-means singular value decomposition) algorithm to patches of dictionary. Proposed method optimized dictionary selection, and with KSVD yielded better video denoising than KSVD with fixed dictionary.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.70(04) [April 2011]en_US
dc.subjectDenoisingen_US
dc.subjectKSVDen_US
dc.subjectOMPen_US
dc.subjectVideoen_US
dc.titleVideo denoising without motion estimation using K-means clusteringen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.70(04) [April 2011]

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