Contrast enhancement using adaptively modified histogram equalization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyoung-Joon | - |
dc.contributor.author | Lee, Jong-Myung | - |
dc.contributor.author | Lee, Jin-Aeon | - |
dc.contributor.author | Oh, Sang-Geun | - |
dc.contributor.author | Kim, Whoi Yul | - |
dc.date.accessioned | 2022-12-21T09:39:10Z | - |
dc.date.available | 2022-12-21T09:39:10Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2006-12 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180630 | - |
dc.description.abstract | A 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.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Contrast enhancement using adaptively modified histogram equalization | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Whoi Yul | - |
dc.identifier.doi | 10.1007/11949534_116 | - |
dc.identifier.scopusid | 2-s2.0-70349689075 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4319 LNCS, pp.1150 - 1158 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 4319 LNCS | - |
dc.citation.startPage | 1150 | - |
dc.citation.endPage | 1158 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Equalizers | - |
dc.subject.keywordPlus | Graphic methods | - |
dc.subject.keywordPlus | Probability density function | - |
dc.subject.keywordPlus | Contrast Enhancement | - |
dc.subject.keywordPlus | Histogram equalizations | - |
dc.subject.keywordPlus | Original images | - |
dc.subject.keywordPlus | Scale Factor | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordAuthor | Contrast enhancement | - |
dc.subject.keywordAuthor | Histogram equalization | - |
dc.subject.keywordAuthor | Image enhancement | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/11949534_116 | - |
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