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Article: Quantitative evaluation of some edge-preserving noise-smoothing techniques

TitleQuantitative evaluation of some edge-preserving noise-smoothing techniques
Authors
KeywordsSIGNAL FILTERING AND PREDICTION
Issue Date1983
Citation
Computer Vision, Graphics And Image Processing, 1983, v. 23 n. 1, p. 67-91 How to Cite?
AbstractA quantitative evaluation of several edge-preserving noise-smoothing techniques is presented. All of the techniques evaluated are devised to preserve edge sharpness while achieving some degree of noise cleaning. They are based on local operations on neighboring points and all of them can be iterated. They are unweighted neighbor averaging (AVE), K-nearest neighbor averaging (KAVE), the edge and line weights method (EDLN), gradient inverse weighted smoothing (GRADIN), maximum homogeneity smoothing (MAXH), slope facet model smoothing (FACET), and median filtering (MEDIAN). The evaluation procedure involves two steps. First, the image is partitioned into regions based on the amount of spatial activity in a neighborhood of a pixel, where spatial activity is defined as local gradient. In the second part of the procedure an objective measure, the mean-square error, for each region of the partitioned image is obtained to evaluate the performance of the smoothing scheme at the corresponding level of spatial activity content. This evaluation procedure provides a convenient way to compare both the edge-preserving and noise-smoothing abilities of different schemes. The smoothing schemes were tested on a specially generated image with varying degrees of added noise and different edge slopes. The results of the comparison study are presented. © 1983.
Persistent Identifierhttp://hdl.handle.net/10722/65520
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChin, RTen_HK
dc.contributor.authorYeh, CLen_HK
dc.date.accessioned2010-08-31T07:15:03Z-
dc.date.available2010-08-31T07:15:03Z-
dc.date.issued1983en_HK
dc.identifier.citationComputer Vision, Graphics And Image Processing, 1983, v. 23 n. 1, p. 67-91en_HK
dc.identifier.issn0734-189Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/65520-
dc.description.abstractA quantitative evaluation of several edge-preserving noise-smoothing techniques is presented. All of the techniques evaluated are devised to preserve edge sharpness while achieving some degree of noise cleaning. They are based on local operations on neighboring points and all of them can be iterated. They are unweighted neighbor averaging (AVE), K-nearest neighbor averaging (KAVE), the edge and line weights method (EDLN), gradient inverse weighted smoothing (GRADIN), maximum homogeneity smoothing (MAXH), slope facet model smoothing (FACET), and median filtering (MEDIAN). The evaluation procedure involves two steps. First, the image is partitioned into regions based on the amount of spatial activity in a neighborhood of a pixel, where spatial activity is defined as local gradient. In the second part of the procedure an objective measure, the mean-square error, for each region of the partitioned image is obtained to evaluate the performance of the smoothing scheme at the corresponding level of spatial activity content. This evaluation procedure provides a convenient way to compare both the edge-preserving and noise-smoothing abilities of different schemes. The smoothing schemes were tested on a specially generated image with varying degrees of added noise and different edge slopes. The results of the comparison study are presented. © 1983.en_HK
dc.languageengen_HK
dc.relation.ispartofComputer Vision, Graphics and Image Processingen_HK
dc.subjectSIGNAL FILTERING AND PREDICTIONen_HK
dc.titleQuantitative evaluation of some edge-preserving noise-smoothing techniquesen_HK
dc.typeArticleen_HK
dc.identifier.emailChin, RT: rchin@hku.hken_HK
dc.identifier.authorityChin, RT=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.scopuseid_2-s2.0-0020971746en_HK
dc.identifier.volume23en_HK
dc.identifier.issue1en_HK
dc.identifier.spage67en_HK
dc.identifier.epage91en_HK
dc.identifier.scopusauthoridChin, RT=7102445426en_HK
dc.identifier.scopusauthoridYeh, CL=7401671792en_HK

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