Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Seonghwan | - |
dc.contributor.author | Kim, Youhyun | - |
dc.contributor.author | Moon, Inkyu | - |
dc.date.accessioned | 2024-06-03T13:10:12Z | - |
dc.date.available | 2024-06-03T13:10:12Z | - |
dc.date.created | 2024-05-16 | - |
dc.date.issued | 2024-09 | - |
dc.identifier.issn | 0030-3992 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/56626 | - |
dc.description.abstract | Digital holography can provide quantitative phase images that are related to the shape and content of biological samples. In particular, high-resolution phase images contain more important details and information about the sample. However, to obtain a high-resolution phase image, various phase reconstruction processes must be performed, while the finite pixel size of the sensor needs to be overcome. We propose a deep learning model that can obtain high-resolution phase images from low-resolution holograms. The proposed model consists of image translation and super-resolution parts, and performs phase reconstruction and the super-resolution process at the same time. We successfully generated sophisticated phase values that closely resembled real images for three scaling factors of (×2, ×3, and ×4). Comparative evaluations with various deep learning models demonstrated the favorable performance of our proposed model. Multi-scale training was also possible, so it was shown that high-resolution phase images could be generated regardless of the scale factor. The proposed model can automatically generate accurate high-resolution phase images from low-resolution holograms, reducing the cost of digital holography, and providing great benefits to biological sample measurement processes. © 2024 Elsevier Ltd | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.title | Automated phase reconstruction and super-resolution with deep learning in digital holography | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.optlastec.2024.111030 | - |
dc.identifier.wosid | 001230983600001 | - |
dc.identifier.scopusid | 2-s2.0-85191653002 | - |
dc.identifier.bibliographicCitation | Optics and Laser Technology, v.176 | - |
dc.description.isOpenAccess | FALSE | - |
dc.subject.keywordAuthor | Digital holography | - |
dc.subject.keywordAuthor | Phase reconstruction | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Super-resolution | - |
dc.subject.keywordAuthor | Image-to-image translation | - |
dc.subject.keywordPlus | NUMERICAL RECONSTRUCTION | - |
dc.subject.keywordPlus | ABERRATION COMPENSATION | - |
dc.subject.keywordPlus | UNWRAPPING ALGORITHM | - |
dc.subject.keywordPlus | MICROSCOPY | - |
dc.subject.keywordPlus | CONTRAST | - |
dc.citation.title | Optics and Laser Technology | - |
dc.citation.volume | 176 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Optics; Physics | - |
dc.relation.journalWebOfScienceCategory | Optics; Physics, Applied | - |
dc.type.docType | Article | - |
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