Referenceless perceptual fog density prediction model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, L.K. | - |
dc.contributor.author | You, Jae hee | - |
dc.contributor.author | Bovik, A.C. | - |
dc.date.accessioned | 2021-10-12T08:44:08Z | - |
dc.date.available | 2021-10-12T08:44:08Z | - |
dc.date.created | 2021-10-08 | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/16418 | - |
dc.description.abstract | We 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.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPIE | - |
dc.subject | Algorithms | - |
dc.subject | Mathematical models | - |
dc.subject | Visibility | - |
dc.subject | Camera information | - |
dc.subject | defog algorithm assessment | - |
dc.subject | Density prediction model | - |
dc.subject | Image database | - |
dc.subject | Natural scene statistics | - |
dc.subject | Salient objects | - |
dc.subject | Statistical features | - |
dc.subject | Statistical regularity | - |
dc.subject | Fog | - |
dc.title | Referenceless perceptual fog density prediction model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | You, Jae hee | - |
dc.identifier.doi | 10.1117/12.2036477 | - |
dc.identifier.scopusid | 2-s2.0-84897492838 | - |
dc.identifier.bibliographicCitation | Proceedings of SPIE - The International Society for Optical Engineering, v.9014 | - |
dc.relation.isPartOf | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.citation.title | Proceedings of SPIE - The International Society for Optical Engineering | - |
dc.citation.volume | 9014 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Mathematical models | - |
dc.subject.keywordPlus | Visibility | - |
dc.subject.keywordPlus | Camera information | - |
dc.subject.keywordPlus | defog algorithm assessment | - |
dc.subject.keywordPlus | Density prediction model | - |
dc.subject.keywordPlus | Image database | - |
dc.subject.keywordPlus | Natural scene statistics | - |
dc.subject.keywordPlus | Salient objects | - |
dc.subject.keywordPlus | Statistical features | - |
dc.subject.keywordPlus | Statistical regularity | - |
dc.subject.keywordPlus | Fog | - |
dc.subject.keywordAuthor | defog algorithm assessment | - |
dc.subject.keywordAuthor | Fog | - |
dc.subject.keywordAuthor | fog aware | - |
dc.subject.keywordAuthor | fog density | - |
dc.subject.keywordAuthor | fog visibility | - |
dc.subject.keywordAuthor | natural scene statistics | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.