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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)6652-6671
Number of pages20
JournalMathematical Biosciences and Engineering
Volume18
Issue number5
DOIs
Publication statusPublished - 2021

Keywords

  • Data encryption
  • Generative adversarial networks
  • Image super-resolution
  • Internet of Things
  • Self-attention

ASJC Scopus subject areas

  • Modelling and Simulation
  • Agricultural and Biological Sciences(all)
  • Computational Mathematics
  • Applied Mathematics

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