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Contrast enhancement using adaptively modified histogram equalization

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dc.contributor.authorKim, Hyoung-Joon-
dc.contributor.authorLee, Jong-Myung-
dc.contributor.authorLee, Jin-Aeon-
dc.contributor.authorOh, Sang-Geun-
dc.contributor.authorKim, Whoi Yul-
dc.date.accessioned2022-12-21T09:39:10Z-
dc.date.available2022-12-21T09:39:10Z-
dc.date.created2022-09-16-
dc.date.issued2006-12-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180630-
dc.description.abstractA new contrast enhancement method called adaptively modified histogram equalization (AMHE) is proposed as an extension of typical histogram equalization. To prevent any significant change of gray levels between the original image and the histogram equalized image, the AMHE scales the magnitudes of the probability density function of the original image before equalization. The scale factor is determined adaptively based on the mean brightness of the original image. The experimental results indicate that the proposed method not only enhances contrast effectively, but also keeps the tone of the original image.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleContrast enhancement using adaptively modified histogram equalization-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Whoi Yul-
dc.identifier.doi10.1007/11949534_116-
dc.identifier.scopusid2-s2.0-70349689075-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4319 LNCS, pp.1150 - 1158-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume4319 LNCS-
dc.citation.startPage1150-
dc.citation.endPage1158-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusEqualizers-
dc.subject.keywordPlusGraphic methods-
dc.subject.keywordPlusProbability density function-
dc.subject.keywordPlusContrast Enhancement-
dc.subject.keywordPlusHistogram equalizations-
dc.subject.keywordPlusOriginal images-
dc.subject.keywordPlusScale Factor-
dc.subject.keywordPlusImage enhancement-
dc.subject.keywordAuthorContrast enhancement-
dc.subject.keywordAuthorHistogram equalization-
dc.subject.keywordAuthorImage enhancement-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/11949534_116-
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