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Referenceless perceptual fog density prediction model

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dc.contributor.authorChoi, L.K.-
dc.contributor.authorYou, Jae hee-
dc.contributor.authorBovik, A.C.-
dc.date.accessioned2021-10-12T08:44:08Z-
dc.date.available2021-10-12T08:44:08Z-
dc.date.created2021-10-08-
dc.date.issued2014-
dc.identifier.issn0277-786X-
dc.identifier.urihttps://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/16418-
dc.description.abstractWe propose a perceptual fog density prediction model based on natural scene statistics (NSS) and for verification statistical features, which can predict the visibility in a foggy scene from a single image without reference to a corresponding fogless image, without side geographical camera information, without training on human-rated judgments, and without dependency on salient objects such as lane markings or traffic signs. The proposed fog density predictor only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. A fog aware collection of statistical features is derived from a corpus of foggy and fog-free images by using a space domain NSS model and observed characteristics of foggy images such as low contrast, faint color, and shifted intensity. The proposed model not only predicts perceptual fog density for the entire image but also provides a local fog density index for each patch. The predicted fog density of the model correlates well with the measured visibility in a foggy scene as measured by judgments taken in a human subjective study on a large foggy image database. As one application, the proposed model accurately evaluates the performance of defog algorithms designed to enhance the visibility of foggy images.. © 2014 SPIE-IS&T.-
dc.language영어-
dc.language.isoen-
dc.publisherSPIE-
dc.subjectAlgorithms-
dc.subjectMathematical models-
dc.subjectVisibility-
dc.subjectCamera information-
dc.subjectdefog algorithm assessment-
dc.subjectDensity prediction model-
dc.subjectImage database-
dc.subjectNatural scene statistics-
dc.subjectSalient objects-
dc.subjectStatistical features-
dc.subjectStatistical regularity-
dc.subjectFog-
dc.titleReferenceless perceptual fog density prediction model-
dc.typeArticle-
dc.contributor.affiliatedAuthorYou, Jae hee-
dc.identifier.doi10.1117/12.2036477-
dc.identifier.scopusid2-s2.0-84897492838-
dc.identifier.bibliographicCitationProceedings of SPIE - The International Society for Optical Engineering, v.9014-
dc.relation.isPartOfProceedings of SPIE - The International Society for Optical Engineering-
dc.citation.titleProceedings of SPIE - The International Society for Optical Engineering-
dc.citation.volume9014-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusAlgorithms-
dc.subject.keywordPlusMathematical models-
dc.subject.keywordPlusVisibility-
dc.subject.keywordPlusCamera information-
dc.subject.keywordPlusdefog algorithm assessment-
dc.subject.keywordPlusDensity prediction model-
dc.subject.keywordPlusImage database-
dc.subject.keywordPlusNatural scene statistics-
dc.subject.keywordPlusSalient objects-
dc.subject.keywordPlusStatistical features-
dc.subject.keywordPlusStatistical regularity-
dc.subject.keywordPlusFog-
dc.subject.keywordAuthordefog algorithm assessment-
dc.subject.keywordAuthorFog-
dc.subject.keywordAuthorfog aware-
dc.subject.keywordAuthorfog density-
dc.subject.keywordAuthorfog visibility-
dc.subject.keywordAuthornatural scene statistics-
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