國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy./
Author:	Patra, Shashwat Rajan.
Description:	1 online resource (67 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (67 pages)

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

Thesis (M.S.)--The University of Memphis, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379403379Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Deep learning methodsIndex Terms--Genre/Form:

554714
Electronic books.

Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy.
LDR:02450ntm a22003977 4500 001 1144247
005 20240531083809.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379403379
035 $a (MiAaPQ)AAI30246886
035 $a AAI30246886
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Patra, Shashwat Rajan. $3 1469208
245 1 0 $a Exploring Deep Learning Methods for Low Numerical Aperture to High Numerical Aperture Resolution Enhancement in Confocal Microscopy.
264 0 $c 2023
300 $a 1 online resource (67 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-10.
500 $a Advisor: Doblas, Ana.
502 $a Thesis (M.S.)--The University of Memphis, 2023.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Optics. $3 595336
650 4 $a Electrical engineering. $3 596380
653 $a Deep learning methods
653 $a Low numerical aperture
653 $a High numerical aperture
653 $a Confocal microscopy
653 $a Optically sectioned images
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0752
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Memphis. $b Electrical & Computer Engineering. $3 1469209
773 0 $t Masters Abstracts International $g 84-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30246886 $z click for full text (PQDT)