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예제학습 방법에 기반한 저해상도 얼굴 영상 복원

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dc.contributor.author이준태-
dc.contributor.author김재협-
dc.contributor.author문영식-
dc.date.accessioned2021-06-23T17:04:15Z-
dc.date.available2021-06-23T17:04:15Z-
dc.date.issued2008-10-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/42124-
dc.description.abstractIn this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images arc hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.-
dc.format.extent2-
dc.language한국어-
dc.language.isoKOR-
dc.publisher대한전기학회-
dc.title예제학습 방법에 기반한 저해상도 얼굴 영상 복원-
dc.title.alternativeFace Hallucination based on Example-Learning-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.bibliographicCitation대한전자공학회 2008 CICS 정보 및 제어 학술대회 논문집, pp 292 - 293-
dc.citation.title대한전자공학회 2008 CICS 정보 및 제어 학술대회 논문집-
dc.citation.startPage292-
dc.citation.endPage293-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassother-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE01348380-
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