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Exploring Deep Learning Methods for ...

Patra, Shashwat Rajan,

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Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy // Shashwat Rajan Patra.
Author:	Patra, Shashwat Rajan,
Description:	1 electronic resource (67 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Computer engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30246886
ISBN:	9798379403379

Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy /
Patra, Shashwat Rajan,

Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy /Shashwat Rajan Patra. - 1 electronic resource (67 pages)

Source: Masters Abstracts International, Volume: 84-10.

Confocal microscopy is a widely used tool that provides valuable morphological and functional information within cells and tissues. A major advantage of confocal microscopy is its ability to record multi-color and optically sectioned images. A major drawback to confocal microscopy is its diffraction-limited spatial resolution. Though techniques have been developed that break this limit in confocal microscopy, they require additional hardware or accurate estimates of the system's impulse response (e.g., point spread function). Here we investigate two deep learning-based models, the cGAN and cycleGAN, trained with low-resolution (LR) and high-resolution (HR) confocal images to improve spatial resolution in confocal microscopy. Our findings conclude that the cGAN can accurately produce HR images if the training set contains images with a high signal-to-noise ratio. We have also found that the cycleGAN model has the potential to perform as the cGAN model but without the requirement of using paired inputs.

English

ISBN: 9798379403379Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Deep learning methods

Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy /
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245 1 0 $a Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy / $c Shashwat Rajan Patra.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (67 pages)
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500 $a Source: Masters Abstracts International, Volume: 84-10.
500 $a Advisors: Doblas, Ana Committee members: Balasubramanian, Madhusudhanan; Skalli, Omar.
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520 $a Confocal microscopy is a widely used tool that provides valuable morphological and functional information within cells and tissues. A major advantage of confocal microscopy is its ability to record multi-color and optically sectioned images. A major drawback to confocal microscopy is its diffraction-limited spatial resolution. Though techniques have been developed that break this limit in confocal microscopy, they require additional hardware or accurate estimates of the system's impulse response (e.g., point spread function). Here we investigate two deep learning-based models, the cGAN and cycleGAN, trained with low-resolution (LR) and high-resolution (HR) confocal images to improve spatial resolution in confocal microscopy. Our findings conclude that the cGAN can accurately produce HR images if the training set contains images with a high signal-to-noise ratio. We have also found that the cycleGAN model has the potential to perform as the cGAN model but without the requirement of using paired inputs.
546 $a English
590 $a School code: 1194
650 4 $a Computer engineering. $3 621879
650 4 $a Optics. $3 517925
650 4 $a Electrical engineering. $3 649834
653 $a Deep learning methods
653 $a Low numerical aperture
653 $a High numerical aperture
653 $a Confocal microscopy
653 $a Optically sectioned images
690 $a 0464
690 $a 0752
690 $a 0544
710 2 $a The University of Memphis. $b Electrical & Computer Engineering. $e degree granting institution. $3 3765737
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773 0 $t Masters Abstracts International $g 84-10.
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791 $a M.S.
792 $a 2023
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