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Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico

Lookup NU author(s): Dr Anurag SharmaORCiD, Dr Muhammad Ramadan SaifuddinORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2024 by the authors.Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net (‘bead’) architectures (termed ‘Θ-Net’ in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology).

Publication metadata

Author(s): Kaderuppan SS, Sharma A, Saifuddin MR, Wong WLE, Woo WL

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2024

Volume: 24

Issue: 19

Online publication date: 29/09/2024

Acceptance date: 23/09/2024

Date deposited: 28/10/2024

ISSN (electronic): 1424-8220

Publisher: Multidisciplinary Digital Publishing Institute (MDPI)

URL: https://doi.org/10.3390/s24196248

DOI: 10.3390/s24196248

Data Access Statement: The data utilized for this study (figures, models and codes) may be downloaded at https://drive.google.com/file/d/1J0HYPE6-tOJOJ9F_fkifzomQ6oFxJ-Mc/view?usp=sharing (accessed on 22 September 2024)

PubMed id: 39409287

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