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

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)6652-6671
ページ数20
ジャーナルMathematical Biosciences and Engineering
18
5
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • モデリングとシミュレーション
  • 農業および生物科学(全般)
  • 計算数学
  • 応用数学

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