Image super-resolution reconstruction for secure data transmission in Internet of Things environment

The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neura...

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Autores principales:	Hongan Li, Qiaoxue Zheng, Wenjing Yan, Ruolin Tao, Xin Qi, Zheng Wen
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	image super-resolution self-attention generative adversarial networks data encryption internet of things Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a32bbb6e712b44a5b3a8747f7107283a
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id	oai:doaj.org-article:a32bbb6e712b44a5b3a8747f7107283a
record_format	dspace
spelling	oai:doaj.org-article:a32bbb6e712b44a5b3a8747f7107283a2021-11-11T02:02:46ZImage super-resolution reconstruction for secure data transmission in Internet of Things environment10.3934/mbe.20213301551-0018https://doaj.org/article/a32bbb6e712b44a5b3a8747f7107283a2021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021330?viewType=HTMLhttps://doaj.org/toc/1551-0018The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.Hongan Li Qiaoxue ZhengWenjing YanRuolin TaoXin Qi Zheng WenAIMS Pressarticleimage super-resolutionself-attentiongenerative adversarial networksdata encryptioninternet of thingsBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6652-6671 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image super-resolution self-attention generative adversarial networks data encryption internet of things Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	image super-resolution self-attention generative adversarial networks data encryption internet of things Biotechnology TP248.13-248.65 Mathematics QA1-939 Hongan Li Qiaoxue Zheng Wenjing Yan Ruolin Tao Xin Qi Zheng Wen Image super-resolution reconstruction for secure data transmission in Internet of Things environment
description	The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.
format	article
author	Hongan Li Qiaoxue Zheng Wenjing Yan Ruolin Tao Xin Qi Zheng Wen
author_facet	Hongan Li Qiaoxue Zheng Wenjing Yan Ruolin Tao Xin Qi Zheng Wen
author_sort	Hongan Li
title	Image super-resolution reconstruction for secure data transmission in Internet of Things environment
title_short	Image super-resolution reconstruction for secure data transmission in Internet of Things environment
title_full	Image super-resolution reconstruction for secure data transmission in Internet of Things environment
title_fullStr	Image super-resolution reconstruction for secure data transmission in Internet of Things environment
title_full_unstemmed	Image super-resolution reconstruction for secure data transmission in Internet of Things environment
title_sort	image super-resolution reconstruction for secure data transmission in internet of things environment
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/a32bbb6e712b44a5b3a8747f7107283a
work_keys_str_mv	AT honganli imagesuperresolutionreconstructionforsecuredatatransmissionininternetofthingsenvironment AT qiaoxuezheng imagesuperresolutionreconstructionforsecuredatatransmissionininternetofthingsenvironment AT wenjingyan imagesuperresolutionreconstructionforsecuredatatransmissionininternetofthingsenvironment AT ruolintao imagesuperresolutionreconstructionforsecuredatatransmissionininternetofthingsenvironment AT xinqi imagesuperresolutionreconstructionforsecuredatatransmissionininternetofthingsenvironment AT zhengwen imagesuperresolutionreconstructionforsecuredatatransmissionininternetofthingsenvironment
_version_	1718439610333265920

Image super-resolution reconstruction for secure data transmission in Internet of Things environment

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