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

Hongan Li; Qiaoxue Zheng; Wenjing Yan; Ruolin Tao; Xin Qi; Zheng Wen; Hongan Li; Qiaoxue Zheng; Wenjing Yan; Ruolin Tao; Xin Qi; Zheng Wen

doi:10.3934/mbe.2021330

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6652-6671. doi: 10.3934/mbe.2021330

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Research article Special Issues

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

1.
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Department of Information Management, School of E-business and Logistics, Beijing Technology and Business University, Beijing 100048, China
3.
Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan
4.
School of Fundamental Science and Engineering, Waseda University, Tokyo 169-8050, Japan

Academic Editor: Weizheng Wang

Received: 23 June 2021 Accepted: 27 July 2021 Published: 06 August 2021

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.
- image super-resolution,
- self-attention,
- generative adversarial networks,
- data encryption,
- Internet of Things
Citation: 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[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6652-6671. doi: 10.3934/mbe.2021330

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Abstract

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.

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