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Conference Paper: A new two-stage method for restoration of images corrupted by Gaussian and impulse noises using local polynomial regression and edge preserving regularization

TitleA new two-stage method for restoration of images corrupted by Gaussian and impulse noises using local polynomial regression and edge preserving regularization
Authors
KeywordsGaussian Noise (Electronic)
Polynomials
Issue Date2009
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2009, p. 948-951 How to Cite?
AbstractThis paper proposes a new two-stage method for restoring image corrupted by additive impulsive and Gaussian noise based on local polynomial regression (LPR) and edge preserving regularization. In LPR, the observations are modeled locally by a polynomial using least-squares criterion with a kernel controlled by a certain bandwidth matrix. A refined intersection confidence intervals (RICI) adaptive scale selector for symmetric kernel is applied in LPR 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 images. The resulting steering-kernel-based LPR with RICI method (SK-LPR-RICI) is applied to smooth images contaminated with Gaussian noise. Furthermore, to remove the impulsive noise in images, an edge-preserving regularization method is employed prior to SK-LPR-RICI and it gives rise to a two-stage method for suppressing both additive impulsive and Gaussian noises. Simulation results show that the proposed method performs satisfactorily and the SK-LPR-RICI method significantly improves the performance after edge-preservation regularization in suppressing the impulsive noise. ©2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143327
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhu, ZYen_HK
dc.date.accessioned2011-11-22T08:30:26Z-
dc.date.available2011-11-22T08:30:26Z-
dc.date.issued2009en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2009, p. 948-951en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143327-
dc.description.abstractThis paper proposes a new two-stage method for restoring image corrupted by additive impulsive and Gaussian noise based on local polynomial regression (LPR) and edge preserving regularization. In LPR, the observations are modeled locally by a polynomial using least-squares criterion with a kernel controlled by a certain bandwidth matrix. A refined intersection confidence intervals (RICI) adaptive scale selector for symmetric kernel is applied in LPR 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 images. The resulting steering-kernel-based LPR with RICI method (SK-LPR-RICI) is applied to smooth images contaminated with Gaussian noise. Furthermore, to remove the impulsive noise in images, an edge-preserving regularization method is employed prior to SK-LPR-RICI and it gives rise to a two-stage method for suppressing both additive impulsive and Gaussian noises. Simulation results show that the proposed method performs satisfactorily and the SK-LPR-RICI method significantly improves the performance after edge-preservation regularization in suppressing the impulsive noise. ©2009 IEEE.en_HK
dc.languageengen_US
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.subjectGaussian Noise (Electronic)en_US
dc.subjectPolynomialsen_US
dc.titleA new two-stage method for restoration of images corrupted by Gaussian and impulse noises using local polynomial regression and edge preserving regularizationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ISCAS.2009.5117914en_HK
dc.identifier.scopuseid_2-s2.0-70350142460en_HK
dc.identifier.hkuros159408-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70350142460&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage948en_HK
dc.identifier.epage951en_HK
dc.identifier.scopusauthoridZhang, ZG=8407277900en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhu, ZY=35099701000en_HK

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