O-Net: A Fast and Precise Deep-Learning Architecture for Computational Super-Resolved Phase-Modulated Optical Microscopy

Kaderuppan, Shiraz S., Wong, Wai Leong Eugene, Sharma, Anurag and Woo, Wai Lok (2022) O-Net: A Fast and Precise Deep-Learning Architecture for Computational Super-Resolved Phase-Modulated Optical Microscopy. Microscopy and Microanalysis. ISSN 1431-9276 (In Press)

[img] Text
O_Net_Manuscript_own_version.pdf - Accepted Version
Restricted to Repository staff only until 15 December 2022.

Download (1MB) | Request a copy
Official URL: https://doi.org/10.1017/S1431927622000782

Abstract

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.

Item Type: Article
Uncontrolled Keywords: super-resolution microscopy, phase-modulated microscopy, computational nanoscopy, deep learning, optical microscopy
Subjects: C500 Microbiology
G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Depositing User: Rachel Branson
Date Deposited: 16 Jun 2022 09:50
Last Modified: 16 Jun 2022 10:09
URI: http://nrl.northumbria.ac.uk/id/eprint/49325

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics