Event-based video deblurring based on image and event feature fusion
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jeongmin | - |
dc.contributor.author | Ghosh, Dipon Kumar | - |
dc.contributor.author | Jung, Yong Ju | - |
dc.date.accessioned | 2023-05-16T01:40:51Z | - |
dc.date.available | 2023-05-16T01:40:51Z | - |
dc.date.created | 2023-05-15 | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87718 | - |
dc.description.abstract | Event-based video deblurring is a method that performs deblurring by taking the event sequence data obtained from an event camera, which is composed of bio-inspired sensors, along with blurry frames as input. Event -based video deblurring has gained attention as a method that can overcome the limitations of conventional frame-based video deblurring. In this study, we propose a novel event-based video deblurring network based on convolution neural networks (CNNs). Unlike the existing event-based deblurring methods that only use event data, the proposed method fuses all the available information from current blurry frames, previously recovered sharp frames, and event data to deblur a video. Specifically, we propose an image and event feature fusion (IEFF) module to fuse event data with current intensity frame information. Additionally, we propose a current-frame reconstruction from previous-frame (CRP) module for acquiring a pseudo sharp frame from a previously recovered sharp frame and a fusion-based residual estimation (FRE) module, which fuses the event features with the image features of the previous sharp frame extracted from the CRP module. We demonstrate through a verification experiment using synthetic and real datasets that the proposed method has superior quantitative and qualitative results compared to state-of-the-art methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.title | Event-based video deblurring based on image and event feature fusion | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000961298900001 | - |
dc.identifier.doi | 10.1016/j.eswa.2023.119917 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.223 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85151476620 | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 223 | - |
dc.contributor.affiliatedAuthor | Kim, Jeongmin | - |
dc.contributor.affiliatedAuthor | Ghosh, Dipon Kumar | - |
dc.contributor.affiliatedAuthor | Jung, Yong Ju | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Event-based vision | - |
dc.subject.keywordAuthor | Motion blur | - |
dc.subject.keywordAuthor | Event-based video deblurring | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon 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.