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Smart Nanoscopy: A Review of Computational Approaches to achieve Super-resolved Optical Microscopy

Lookup NU author(s): Dr Anurag Sharma, Dr Wai Lok Woo

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


Abstract

CCBYThe field of optical nanoscopy, a paradigm referring to the recent cutting-edge developments aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical microscopy, has gained recent prominence & traction in the 21st century. Numerous optical implementations allowing for a new frontier in traditional confocal laser scanning fluorescence microscopy to be explored (termed super-resolution fluorescence microscopy) have been realized through the development of techniques such as stimulated emission and depletion (STED) microscopy, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), amongst others. Nonetheless, it would be apt to mention at this juncture that optical nanoscopy has been explored since the mid-late 20th century, through several computational techniques such as deblurring and deconvolution algorithms. In this review, we take a step back in the field, evaluating the various in silico methods used to achieve optical nanoscopy today, ranging from traditional deconvolution algorithms (such as the Nearest Neighbors algorithm) to the latest developments in the field of computational nanoscopy, founded on artificial intelligence (AI). An insight is provided into some of the commercial applications of AI-based super-resolution imaging, prior to delving into the potentially promising future implications of computational nanoscopy. This is facilitated by recent advancements in the field of AI, deep learning (DL) and convolutional neural network (CNN) architectures, coupled with the growing size of data sources and rapid improvements in computing hardware, such as multi-core CPUs & GPUs, low-latency RAM and hard-drive capacities.


Publication metadata

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

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2020

Volume: 8

Pages: 214801 - 214831

Online publication date: 24/11/2020

Acceptance date: 07/10/2020

Date deposited: 18/12/2020

ISSN (electronic): 2169-3536

Publisher: Institute of Electrical and Electronics Engineers Inc.

URL: https://doi.org/10.1109/ACCESS.2020.3040319

DOI: 10.1109/ACCESS.2020.3040319


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