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Conference Paper: RESTORATION OF IMAGES WITH NONSTATIONARY MEAN AND AUTOCORRELATION.

TitleRESTORATION OF IMAGES WITH NONSTATIONARY MEAN AND AUTOCORRELATION.
Authors
KeywordsSIGNAL FILTERING AND PREDICTION
STATISTICAL METHODS
Issue Date1988
Citation
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 1988, p. 1008-1011 How to Cite?
AbstractMethods are investigated for the restoration of images degraded by both blur and noise. The objective is to develop estimation strategies to deal with images that exhibit spatially varying statistics. The restoration starts with transforming the image with nonstationary statistics into an image that exhibits stationary characteristics. This transformation can be viewed as a prewhitening filter that normalizes the local mean and local variance of the image, creating a stationary, or near stationary, field. Then the ideal image is estimated from the transformed image on the basis of the linear minimum-mean-square-error criterion. The process removes image blur and noise and at the same time inverts the effects of the transformation.
Persistent Identifierhttp://hdl.handle.net/10722/65573
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHillery, Allen Den_HK
dc.contributor.authorChin, Roland Ten_HK
dc.date.accessioned2010-08-31T07:16:12Z-
dc.date.available2010-08-31T07:16:12Z-
dc.date.issued1988en_HK
dc.identifier.citationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 1988, p. 1008-1011en_HK
dc.identifier.issn0736-7791en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65573-
dc.description.abstractMethods are investigated for the restoration of images degraded by both blur and noise. The objective is to develop estimation strategies to deal with images that exhibit spatially varying statistics. The restoration starts with transforming the image with nonstationary statistics into an image that exhibits stationary characteristics. This transformation can be viewed as a prewhitening filter that normalizes the local mean and local variance of the image, creating a stationary, or near stationary, field. Then the ideal image is estimated from the transformed image on the basis of the linear minimum-mean-square-error criterion. The process removes image blur and noise and at the same time inverts the effects of the transformation.en_HK
dc.languageengen_HK
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsen_HK
dc.subjectSIGNAL FILTERING AND PREDICTIONen_HK
dc.subjectSTATISTICAL METHODSen_HK
dc.titleRESTORATION OF IMAGES WITH NONSTATIONARY MEAN AND AUTOCORRELATION.en_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChin, Roland T: rchin@hku.hken_HK
dc.identifier.authorityChin, Roland T=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.scopuseid_2-s2.0-0023708881en_HK
dc.identifier.spage1008en_HK
dc.identifier.epage1011en_HK
dc.identifier.scopusauthoridHillery, Allen D=7003403093en_HK
dc.identifier.scopusauthoridChin, Roland T=7102445426en_HK

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