O-Net: A Fast and Precise Deep-Learning Architecture for Computational Super-Resolved Phase-Modulated Optical Microscopy
| العنوان: | O-Net: A Fast and Precise Deep-Learning Architecture for Computational Super-Resolved Phase-Modulated Optical Microscopy |
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| المؤلفون: | Shiraz S Kaderuppan, Wai Leong Eugene Wong, Anurag Sharma, Wai Lok Woo |
| المصدر: | Microscopy and Microanalysis. 28:1584-1598 |
| بيانات النشر: | Oxford University Press (OUP), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | G400, G900, C500, Instrumentation |
| الوصف: | We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-resolved images from conventional phase-modulated optical microscopical techniques, such as phase-contrast microscopy and differential interference contrast microscopy. O-Net represents a novel deep convolutional neural network that can be trained on both simulated and experimental data, the latter of which is being demonstrated in the present context. The present study demonstrates the ability of the proposed method to achieve super-resolved images even under poor signal-to-noise ratios and does not require prior information on the point spread function or optical character of the system. Moreover, unlike previous state-of-the-art deep neural networks (such as U-Nets), the O-Net architecture seemingly demonstrates an immunity to network hallucination, a commonly cited issue caused by network overfitting when U-Nets are employed. Models derived from the proposed O-Net architecture are validated through empirical comparison with a similar sample imaged via scanning electron microscopy (SEM) and are found to generate ultra-resolved images which came close to that of the actual SEM micrograph. |
| وصف الملف: | application/pdf |
| تدمد: | 1435-8115 1431-9276 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83df3c23eb14a66c0873d0782dbbfde9 https://doi.org/10.1017/s1431927622000782 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....83df3c23eb14a66c0873d0782dbbfde9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14358115 14319276 |
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