Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy
The 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 21<sup>st</sup> century. Numerous opt...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9268933/ |
| _version_ | 1818616822143909888 |
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| author | Shiraz S. Kaderuppan Eugene Wai Leong Wong Anurag Sharma Wai Lok Woo |
| author_facet | Shiraz S. Kaderuppan Eugene Wai Leong Wong Anurag Sharma Wai Lok Woo |
| author_sort | Shiraz S. Kaderuppan |
| collection | DOAJ |
| description | The 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 21<sup>st</sup> 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 20<sup>th</sup> 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. |
| first_indexed | 2024-12-16T16:55:54Z |
| format | Article |
| id | doaj.art-0783e6ebc215439e8d2e0388c86679d1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:55:54Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0783e6ebc215439e8d2e0388c86679d12022-12-21T22:23:53ZengIEEEIEEE Access2169-35362020-01-01821480121483110.1109/ACCESS.2020.30403199268933Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical MicroscopyShiraz S. Kaderuppan0https://orcid.org/0000-0002-0317-8272Eugene Wai Leong Wong1https://orcid.org/0000-0003-1146-8285Anurag Sharma2https://orcid.org/0000-0002-0810-4432Wai Lok Woo3https://orcid.org/0000-0002-8698-7605Newcastle Research and Innovation Institute Pte Ltd, (NewRIIS), Devan Nair Institute for Employment and Employability, SingaporeNewcastle Research and Innovation Institute Pte Ltd, (NewRIIS), Devan Nair Institute for Employment and Employability, SingaporeNewcastle Research and Innovation Institute Pte Ltd, (NewRIIS), Devan Nair Institute for Employment and Employability, SingaporeFaculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, U.K.The 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 21<sup>st</sup> 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 20<sup>th</sup> 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.https://ieeexplore.ieee.org/document/9268933/Super-resolution microscopycomputational nanoscopyhigh-resolution microscopical imagingoptical microscopydeep learning |
| spellingShingle | Shiraz S. Kaderuppan Eugene Wai Leong Wong Anurag Sharma Wai Lok Woo Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy IEEE Access Super-resolution microscopy computational nanoscopy high-resolution microscopical imaging optical microscopy deep learning |
| title | Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy |
| title_full | Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy |
| title_fullStr | Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy |
| title_full_unstemmed | Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy |
| title_short | Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy |
| title_sort | smart nanoscopy a review of computational approaches to achieve super resolved optical microscopy |
| topic | Super-resolution microscopy computational nanoscopy high-resolution microscopical imaging optical microscopy deep learning |
| url | https://ieeexplore.ieee.org/document/9268933/ |
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