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Image super-resolution using generative adversarial networks with efficientNetV2.

The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets. The superresolution has potential applications in various domains, such as medical image processing, crime investigation, remote s...

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Main Authors: AlTakrouri,, Saleh, Mohd. Noor, Norliza, Ahmad, Norulhusna, Justinia, Taghreed, Usman, Sahnius
Format: Article
Language:English
Published: Science and Information Organization 2023
Subjects:
T58.5-58.64 Information technology
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/105362/1/SalehAltakrouri2023_ImageSuperResolutionUsingGenerativeAdversarial.pdf
http://eprints.utm.my/105362/
http://dx.doi.org/10.14569/IJACSA.2023.01402100
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spelling my.utm.1053622024-04-24T06:38:23Z http://eprints.utm.my/105362/ Image super-resolution using generative adversarial networks with efficientNetV2. AlTakrouri,, Saleh Mohd. Noor, Norliza Ahmad, Norulhusna Justinia, Taghreed Usman, Sahnius T58.5-58.64 Information technology TA Engineering (General). Civil engineering (General) The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets. The superresolution has potential applications in various domains, such as medical image processing, crime investigation, remote sensing, and other image-processing application domains. The goal of the super-resolution is to obtain the image with minimal mean square error with improved perceptual quality. Therefore, this study introduces the perceptual loss minimization technique through efficient learning criteria. The proposed image reconstruction technique uses the image super-resolution generative adversarial network (ISRGAN), in which the learning of the discriminator in the ISRGAN is performed using the EfficientNet-v2 to obtain a better image quality. The proposed ISRGAN with the EfficientNet-v2 achieved a minimal loss of 0.02, 0.1, and 0.015 at the generator, discriminator, and self-supervised learning, respectively, with a batch size of 32. The minimal mean square error and mean absolute error are 0.001025 and 0.00225, and the maximal peak signal-to-noise ratio and structural similarity index measure obtained are 45.56985 and 0.9997, respectively. Science and Information Organization 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/105362/1/SalehAltakrouri2023_ImageSuperResolutionUsingGenerativeAdversarial.pdf AlTakrouri,, Saleh and Mohd. Noor, Norliza and Ahmad, Norulhusna and Justinia, Taghreed and Usman, Sahnius (2023) Image super-resolution using generative adversarial networks with efficientNetV2. International Journal Of Advanced Computer Science And Applications, 14 (2). pp. 879-887. ISSN 2158-107X http://dx.doi.org/10.14569/IJACSA.2023.01402100 DOI: 10.14569/IJACSA.2023.01402100
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T58.5-58.64 Information technology
TA Engineering (General). Civil engineering (General)
spellingShingle T58.5-58.64 Information technology
TA Engineering (General). Civil engineering (General)
AlTakrouri,, Saleh
Mohd. Noor, Norliza
Ahmad, Norulhusna
Justinia, Taghreed
Usman, Sahnius
Image super-resolution using generative adversarial networks with efficientNetV2.
description The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets. The superresolution has potential applications in various domains, such as medical image processing, crime investigation, remote sensing, and other image-processing application domains. The goal of the super-resolution is to obtain the image with minimal mean square error with improved perceptual quality. Therefore, this study introduces the perceptual loss minimization technique through efficient learning criteria. The proposed image reconstruction technique uses the image super-resolution generative adversarial network (ISRGAN), in which the learning of the discriminator in the ISRGAN is performed using the EfficientNet-v2 to obtain a better image quality. The proposed ISRGAN with the EfficientNet-v2 achieved a minimal loss of 0.02, 0.1, and 0.015 at the generator, discriminator, and self-supervised learning, respectively, with a batch size of 32. The minimal mean square error and mean absolute error are 0.001025 and 0.00225, and the maximal peak signal-to-noise ratio and structural similarity index measure obtained are 45.56985 and 0.9997, respectively.
format Article
author AlTakrouri,, Saleh
Mohd. Noor, Norliza
Ahmad, Norulhusna
Justinia, Taghreed
Usman, Sahnius
author_facet AlTakrouri,, Saleh
Mohd. Noor, Norliza
Ahmad, Norulhusna
Justinia, Taghreed
Usman, Sahnius
author_sort AlTakrouri,, Saleh
title Image super-resolution using generative adversarial networks with efficientNetV2.
title_short Image super-resolution using generative adversarial networks with efficientNetV2.
title_full Image super-resolution using generative adversarial networks with efficientNetV2.
title_fullStr Image super-resolution using generative adversarial networks with efficientNetV2.
title_full_unstemmed Image super-resolution using generative adversarial networks with efficientNetV2.
title_sort image super-resolution using generative adversarial networks with efficientnetv2.
publisher Science and Information Organization
publishDate 2023
url http://eprints.utm.my/105362/1/SalehAltakrouri2023_ImageSuperResolutionUsingGenerativeAdversarial.pdf
http://eprints.utm.my/105362/
http://dx.doi.org/10.14569/IJACSA.2023.01402100
_version_ 1797906004078231552
score 13.244403

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