CNN 영상 회귀 기반의 산란계수 추정을 통한 연무제거
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정원영 | - |
dc.contributor.author | 김선영 | - |
dc.contributor.author | 박찬국 | - |
dc.contributor.author | 강창호 | - |
dc.date.accessioned | 2021-11-17T01:40:08Z | - |
dc.date.available | 2021-11-17T01:40:08Z | - |
dc.date.created | 2021-11-17 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20280 | - |
dc.description.abstract | The estimation of the scattering coefficient in depth image-based dehazing is of paramount importance. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal scattering coefficients for effective dehazing must be obtained depending on the level of haze and fog generation. In this study, we performed a CNN-based image regression to obtain the optimal scattering coefficients for each image with fog and haze. A three-channel image was used as the input data, and the learning was performed with approximately 2,000 labeled synthetic haze and fog datasets. Subsequently, the transmission image was estimated using the scattering coefficient obtained for the input image through the learned model, and the depth image was obtained through the LiDAR point cloud projection for performing the dehazing. This paper presents a qualitative and quantitative comparison of the results obtained using the proposed dehazing technique with those obtained using the existing dehazing algorithms. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 제어·로봇·시스템학회 | - |
dc.title | CNN 영상 회귀 기반의 산란계수 추정을 통한 연무제거 | - |
dc.title.alternative | Image Dehazing with Scattering Coefficient Estimation using CNN-based Image Regression | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 정원영 | - |
dc.contributor.affiliatedAuthor | 강창호 | - |
dc.identifier.doi | 10.5302/J.ICROS.2021.21.0123 | - |
dc.identifier.bibliographicCitation | 제어.로봇.시스템학회 논문지, v.27, no.11, pp.890 - 896 | - |
dc.relation.isPartOf | 제어.로봇.시스템학회 논문지 | - |
dc.citation.title | 제어.로봇.시스템학회 논문지 | - |
dc.citation.volume | 27 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 890 | - |
dc.citation.endPage | 896 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002772906 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | image dehazing | - |
dc.subject.keywordAuthor | CNN | - |
dc.subject.keywordAuthor | LiDAR | - |
dc.subject.keywordAuthor | dark channel prior | - |
dc.subject.keywordAuthor | scattering coefficient | - |
dc.subject.keywordAuthor | image regression | - |
dc.subject.keywordAuthor | . | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh 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.