A transformer-based real-time focus detection technique for wide-field interferometric microscopy
buir.contributor.author | Polat, Can | |
buir.contributor.author | Çukur, Tolga | |
buir.contributor.orcid | Polat, Can|0000-0002-1458-302X | |
buir.contributor.orcid | Çukur, Tolga|0000-0002-2296-851X | |
dc.citation.epage | 4 | en_US |
dc.citation.spage | 1 | |
dc.contributor.author | Polat, Can | |
dc.contributor.author | Güngör, A. | |
dc.contributor.author | Yorulmaz, M. | |
dc.contributor.author | Kızılelma, B. | |
dc.contributor.author | Çukur, Tolga | |
dc.coverage.spatial | İstanbul, Türkiye | |
dc.date.accessioned | 2024-03-22T13:22:23Z | |
dc.date.available | 2024-03-22T13:22:23Z | |
dc.date.issued | 2023-08-28 | |
dc.department | Department of Electrical and Electronics Engineering | |
dc.description | Date of Conference: 05-08 July 2023 | |
dc.description | Conference Name: 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 | |
dc.description.abstract | Wide-field interferometric microscopy (WIM) has been utilized for visualization of individual biological nanoparticles with high sensitivity. However, the image quality is highly affected by the focusing of the image. Hence, focus detection has been an active research field within the scope of imaging and microscopy. To tackle this issue, we propose a novel convolution and transformer based deep learning technique to detect focus in WIM. The method is compared to other focus detecton techniques and is able to obtain higher precision with less number of parameters. Furthermore, the model achieves real-time focus detection thanks to its low inference time. | |
dc.description.abstract | Geniş-alan interferometrik mikroskopi (GİM), tekli biyolojik nanopartiküllerin yüksek hassasiyetle görüntülenmesi için kullanılmaktadır. Ancak, görüntü kalitesi görüntünün odaklanma performansından oldukça etkilenmektedir. Bu sebeple, odak tespiti görüntüleme ve mikroskopi literatüründe aktif çalışılan bir konudur. Bu problemin çözümüne yönelik, GİM odağının tespiti için evrişim ve dönüştürücü tabanlı yenilikçi bir derin öğrenme tekniği önerilmektedir. Yöntem, literatürdeki diğer odak tespit teknikleriyle kıyaslanmış ve daha az sayıda parametre ile daha yüksek başarım elde edilebildiği gösterilmiştir. Ayrıca, yöntem düşük çıkarım zamanı sayesinde gerçek zamanlı odak tespitinde bulunabilmektedir. | |
dc.description.provenance | Made available in DSpace on 2024-03-22T13:22:23Z (GMT). No. of bitstreams: 1 A_transformer-based_real-time_focus_detection_technique_for_wide-field_interferometric_microscopy.pdf: 11111211 bytes, checksum: 761cc4c417c0302c82833f9b5aeda4af (MD5) Previous issue date: 2023-08 | en |
dc.identifier.doi | 10.1109/SIU59756.2023.10223991 | |
dc.identifier.eisbn | 9798350343557 | |
dc.identifier.isbn | 9798350343564 | |
dc.identifier.issn | 2165-0608 | |
dc.identifier.uri | https://hdl.handle.net/11693/115092 | |
dc.language.iso | Turkish | |
dc.publisher | IEEE - Institute of Electrical and Electronics Engineers | |
dc.relation.isversionof | https://dx.doi.org/10.1109/SIU59756.2023.10223991 | |
dc.source.title | 2023 31st Signal Processing and Communications Applications Conference (SIU 2023) | |
dc.subject | Deep learning | |
dc.subject | Focus detection | |
dc.subject | Convolution | |
dc.subject | Transformer | |
dc.subject | Microscopy | |
dc.subject | Real-time | |
dc.subject | Derin öğrenme | |
dc.subject | Odak tespiti | |
dc.subject | Evrişim | |
dc.subject | Dönüştürücü | |
dc.subject | Mikroskop | |
dc.subject | Gerçek-zamanlı | |
dc.title | A transformer-based real-time focus detection technique for wide-field interferometric microscopy | |
dc.title.alternative | Geniş-alan interferometrik mikroskop için dönüştürücü tabanlı gerçek zamanlı odak tespiti tekniği | |
dc.type | Conference Paper |
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