Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Hyeonseung | - |
dc.contributor.author | Kim, Youngrok | - |
dc.contributor.author | Yang, Daeho | - |
dc.contributor.author | Seo, Wontaek | - |
dc.contributor.author | Kim, Yunhee | - |
dc.contributor.author | Hong, Jong-Young | - |
dc.contributor.author | Song, Hoon | - |
dc.contributor.author | Sung, Geeyoung | - |
dc.contributor.author | Sung, Younghun | - |
dc.contributor.author | Min, Sung-Wook | - |
dc.contributor.author | Lee, Hong-Seok | - |
dc.date.accessioned | 2023-09-15T15:41:06Z | - |
dc.date.available | 2023-09-15T15:41:06Z | - |
dc.date.created | 2023-09-15 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89105 | - |
dc.description.abstract | While recent research has shown that holographic displays can represent photorealistic 3D holograms in real time, the difficulty in acquiring high-quality real-world holograms has limited the realization of holographic streaming systems. Incoherent holographic cameras, which record holograms under daylight conditions, are suitable candidates for real-world acquisition, as they prevent the safety issues associated with the use of lasers; however, these cameras are hindered by severe noise due to the optical imperfections of such systems. In this work, we develop a deep learning-based incoherent holographic camera system that can deliver visually enhanced holograms in real time. A neural network filters the noise in the captured holograms, maintaining a complex-valued hologram format throughout the whole process. Enabled by the computational efficiency of the proposed filtering strategy, we demonstrate a holographic streaming system integrating a holographic camera and holographic display, with the aim of developing the ultimate holographic ecosystem of the future. The authors develop a deep learning-based incoherent holographic camera system in order to deliver visually enhanced holograms in real-time. The neural network filters the noise in the captured holograms, and by integrating a holographic camera and a display, they demonstrate a holographic streaming system. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.relation.isPartOf | NATURE COMMUNICATIONS | - |
dc.title | Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 001048208600024 | - |
dc.identifier.doi | 10.1038/s41467-023-39329-0 | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.14, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85161884340 | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 14 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Yang, Daeho | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | DIGITAL HOLOGRAPHY | - |
dc.subject.keywordPlus | NOISE-REDUCTION | - |
dc.subject.keywordPlus | SUPERRESOLUTION | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | RESOLUTION | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
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.