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DC Field | Value | Language |
---|---|---|
dc.citation.number | 1 | - |
dc.citation.startPage | 45 | - |
dc.citation.title | ASTROPHYSICAL JOURNAL | - |
dc.citation.volume | 972 | - |
dc.contributor.author | Park, Hyosun | - |
dc.contributor.author | Jo, Yongsik | - |
dc.contributor.author | Kang, Seokun | - |
dc.contributor.author | Kim, Taehwan | - |
dc.contributor.author | Jee, M. James | - |
dc.date.accessioned | 2024-09-13T11:05:08Z | - |
dc.date.available | 2024-09-13T11:05:08Z | - |
dc.date.created | 2024-09-12 | - |
dc.date.issued | 2024-09 | - |
dc.description.abstract | The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, and time-series forecasting. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We conducted experiments using pairs of high-quality images and their degraded versions, and our deep learning model demonstrates exceptional restoration of photometric, structural, and morphological information. When compared with the ground-truth James Webb Space Telescope images, the enhanced versions of our Hubble Space Telescope-quality images reduce the scatter of isophotal photometry, S & eacute;rsic index, and half-light radius by factors of 4.4, 3.6, and 4.7, respectively, with Pearson correlation coefficients approaching unity. The performance is observed to degrade when input images exhibit correlated noise, point-like sources, and artifacts. We anticipate that this deep learning model will prove valuable for a number of scientific applications, including precision photometry, morphological analysis, and shear calibration. | - |
dc.identifier.bibliographicCitation | ASTROPHYSICAL JOURNAL, v.972, no.1, pp.45 | - |
dc.identifier.doi | 10.3847/1538-4357/ad5954 | - |
dc.identifier.issn | 0004-637X | - |
dc.identifier.scopusid | 2-s2.0-85202072589 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/83780 | - |
dc.identifier.wosid | 001296627900001 | - |
dc.language | 영어 | - |
dc.publisher | IOP Publishing Ltd | - |
dc.title | Deeper, Sharper, Faster: Application of Efficient Transformer to Galaxy Image Restoration | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Astronomy & Astrophysics | - |
dc.relation.journalResearchArea | Astronomy & Astrophysics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | DECONVOLUTION | - |
dc.subject.keywordPlus | ASTROPY | - |
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