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Micro-Cells Enabling Spatio-Angular Joint Attention for High-Resolution Light Field Editing
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Gwon-Jung | - |
| dc.contributor.author | Rhee, Chae Eun | - |
| dc.date.accessioned | 2025-01-02T09:02:02Z | - |
| dc.date.available | 2025-01-02T09:02:02Z | - |
| dc.date.issued | 2024-11 | - |
| dc.identifier.issn | 2169-3536 | - |
| dc.identifier.issn | 2169-3536 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/204231 | - |
| dc.description.abstract | Light Field (LF) processing is a critical step in advancing immersive experiences beyond two-dimensional (2D) images. While LF-based technology facilitates a smooth transition from 2D to three-dimensional (3D) media, processing LFs composed of numerous images remains challenging. Most existing research has provided only limited processing capabilities for low-resolution LFs. For instance, LF editing typically offers basic functions like color changes and object removal. This paper proposes micro-cell-based processing for spatially and angularly large-scale LFs. By partitioning and rearranging the LF into micro-cells, which are then used as input for a vision transformer (ViT), we enable spatio-angular joint attention that significantly enhances performance. The local attention applied to small-sized micro-cells allows for high-resolution LF processing without excessive computational costs. This paper utilizes this combination of micro-cells and the ViT for LF editing applications. Experimental results demonstrate that the proposed LF editing system delivers exceptional performance both quantitatively and qualitatively, enabling high-resolution LF processing with a variety of editing functions. | - |
| dc.format.extent | 12 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
| dc.title | Micro-Cells Enabling Spatio-Angular Joint Attention for High-Resolution Light Field Editing | - |
| dc.type | Article | - |
| dc.publisher.location | 미국 | - |
| dc.identifier.doi | 10.1109/ACCESS.2024.3504730 | - |
| dc.identifier.scopusid | 2-s2.0-85210288416 | - |
| dc.identifier.wosid | 001367268100002 | - |
| dc.identifier.bibliographicCitation | IEEE Access, v.12, pp 175817 - 175828 | - |
| dc.citation.title | IEEE Access | - |
| dc.citation.volume | 12 | - |
| dc.citation.startPage | 175817 | - |
| dc.citation.endPage | 175828 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Computer Science | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalResearchArea | Telecommunications | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
| dc.relation.journalWebOfScienceCategory | Telecommunications | - |
| dc.subject.keywordPlus | Cell culture | - |
| dc.subject.keywordPlus | Microoptics | - |
| dc.subject.keywordAuthor | Geometry | - |
| dc.subject.keywordAuthor | Spatial resolution | - |
| dc.subject.keywordAuthor | Image resolution | - |
| dc.subject.keywordAuthor | Transformers | - |
| dc.subject.keywordAuthor | Light fields | - |
| dc.subject.keywordAuthor | Three-dimensional displays | - |
| dc.subject.keywordAuthor | Scalability | - |
| dc.subject.keywordAuthor | Media | - |
| dc.subject.keywordAuthor | Image color analysis | - |
| dc.subject.keywordAuthor | Deep learning | - |
| dc.subject.keywordAuthor | attention mechanisms | - |
| dc.subject.keywordAuthor | image synthesis | - |
| dc.subject.keywordAuthor | image reconstruction | - |
| dc.identifier.url | https://ieeexplore.ieee.org/document/10763504 | - |
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