WEB OF SCIENCE
SCOPUS
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Park, Seonghwan | - |
| dc.contributor.author | Moon, Inkyu | - |
| dc.date.accessioned | 2025-07-03T20:40:09Z | - |
| dc.date.available | 2025-07-03T20:40:09Z | - |
| dc.date.created | 2025-06-30 | - |
| dc.date.issued | 2025-04-14 | - |
| dc.identifier.isbn | 9781510687196 | - |
| dc.identifier.issn | 0277-786X | - |
| dc.identifier.uri | https://scholar.dgist.ac.kr/handle/20.500.11750/58614 | - |
| dc.description.abstract | In this paper, we present a multispectral photon-counting imaging (PCI) method based on denoising diffusion models for multispectral visualization of virtually photon limited scenes. We measure the accuracy as well as the speed of denoising diffusion algorithms to estimate multispectral scenes at low light levels. Experimental results demonstrate that the proposed deep learning model achieves better performance in terms of peak-to-SNR (PSNR) and faster computation than variational autoencoders. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. | - |
| dc.language | English | - |
| dc.publisher | SPIE(The International Society for Optical Engineering) | - |
| dc.relation.ispartof | Proceedings of SPIE - The International Society for Optical Engineering | - |
| dc.title | Photon-counting imaging with denoising diffusion models | - |
| dc.type | Conference Paper | - |
| dc.identifier.doi | 10.1117/12.3052241 | - |
| dc.identifier.scopusid | 2-s2.0-105008198242 | - |
| dc.identifier.bibliographicCitation | Park, Seonghwan. (2025-04-14). Photon-counting imaging with denoising diffusion models. Three-Dimensional Imaging, Visualization, and Display 2025, 1–7. doi: 10.1117/12.3052241 | - |
| dc.citation.conferenceDate | 2025-04-14 | - |
| dc.citation.conferencePlace | US | - |
| dc.citation.conferencePlace | Orlando | - |
| dc.citation.endPage | 7 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.title | Three-Dimensional Imaging, Visualization, and Display 2025 | - |
