Real-time, deep-learning aided lensless microscope

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dc.citation.firstpage4037
dc.citation.issueNumber8
dc.citation.journalTitleBiomedical Optics Express
dc.citation.lastpage4051
dc.citation.volumeNumber14
dc.contributor.authorWu, Jimin
dc.contributor.authorBoominathan, Vivek
dc.contributor.authorVeeraraghavan, Ashok
dc.contributor.authorRobinson, Jacob T.
dc.date.accessioned2024-05-08T18:56:12Z
dc.date.available2024-05-08T18:56:12Z
dc.date.issued2023
dc.description.abstractTraditional miniaturized fluorescence microscopes are critical tools for modern biology. Invariably, they struggle to simultaneously image with a high spatial resolution and a large field of view (FOV). Lensless microscopes offer a solution to this limitation. However, real-time visualization of samples is not possible with lensless imaging, as image reconstruction can take minutes to complete. This poses a challenge for usability, as real-time visualization is a crucial feature that assists users in identifying and locating the imaging target. The issue is particularly pronounced in lensless microscopes that operate at close imaging distances. Imaging at close distances requires shift-varying deconvolution to account for the variation of the point spread function (PSF) across the FOV. Here, we present a lensless microscope that achieves real-time image reconstruction by eliminating the use of an iterative reconstruction algorithm. The neural network-based reconstruction method we show here, achieves more than 10000 times increase in reconstruction speed compared to iterative reconstruction. The increased reconstruction speed allows us to visualize the results of our lensless microscope at more than 25 frames per second (fps), while achieving better than 7 µm resolution over a FOV of 10 mm2. This ability to reconstruct and visualize samples in real-time empowers a more user-friendly interaction with lensless microscopes. The users are able to use these microscopes much like they currently do with conventional microscopes.
dc.identifier.citationWu, J., Boominathan, V., Veeraraghavan, A., & Robinson, J. T. (2023). Real-time, deep-learning aided lensless microscope. Biomedical Optics Express, 14(8), 4037–4051. https://doi.org/10.1364/BOE.490199
dc.identifier.digitalboe-14-8-4037
dc.identifier.doihttps://doi.org/10.1364/BOE.490199
dc.identifier.urihttps://hdl.handle.net/1911/115692
dc.language.isoeng
dc.publisherOptica Publishing Group
dc.rightsPublished under the terms of the Optica Open Access Publishing Agreement
dc.rights.urihttps://opg.optica.org/library/license_v2.cfm#VOR-OA
dc.titleReal-time, deep-learning aided lensless microscope
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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