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Lookup NU author(s): Dr Anurag Sharma, Dr Wai Lok Woo
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© 2022 The Author(s). Published by Cambridge University Press on behalf of the Microscopy Society of America.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.
Author(s): Kaderuppan SS, Wong WLE, Sharma A, Woo WL
Publication type: Article
Publication status: Published
Journal: Microscopy and Microanalysis
Year: 2022
Volume: 28
Issue: 5
Pages: 1584-1598
Print publication date: 26/10/2022
Online publication date: 15/06/2022
Acceptance date: 02/04/2018
ISSN (print): 1431-9276
ISSN (electronic): 1435-8115
Publisher: Cambridge University Press
URL: https://doi.org/10.1017/S1431927622000782
DOI: 10.1017/S1431927622000782
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