A Secure Cooperative Transmission of Image Super-Resolution in Wireless Relay Networks

被引:0
作者
Duong, Hien-Thuan [1 ,4 ]
Phan, Ca V. [1 ]
Vien, Quoc-Tuan [2 ]
Nguyen, Tuan T. [3 ]
机构
[1] Ho Chi Minh City Univ Technol & Educ, Fac Elect & Elect Engn, Ho Chi Minh City 700000, Vietnam
[2] Middlesex Univ, Fac Sci & Technol, London NW4 4BT, England
[3] Univ Greenwich, Sch Comp & Math Sci, London SE10 9LS, England
[4] Sai Gon Univ, Fac Elect & Telecommun, Ho Chi Minh City 700000, Vietnam
关键词
image communication; deep learning; image super-resolution; random linear network coding; cooperative communications; wireless relay networks; PERFORMANCE ANALYSIS;
D O I
10.3390/electronics12183764
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The image transmission over wireless media experiences not only unavailable performance loss caused by the environment and hardware issues, but also information leakage to eavesdroppers who can overhear and attempt to recover the images. This paper proposes a secure cooperative relaying (SCR) protocol for the image communications in wireless relay networks (WRNs) where Alice sends high-resolution (HR) images to Bob with the assistance of a relaying user named Relay, and in the presence of an eavesdropper named Eve. In order to enhance the security of the image communications, random linear network coding (RLNC) is employed at both Alice and Relay to conceal the original images from Eve with RLNC coefficient matrices and reference images in the shared image datastore. Furthermore, the original HR images are downscaled at Alice to save transmission bandwidth and image super-resolution (ISR) is adopted at Bob due to its capability to recover the HR images from their low-resolution (LR) version, while still maintaining the image quality. In the proposed SCR protocol, Bob can decode both the original images transmitted from Alice over the direct link and the images forwarded by Relay over the relaying links. Simulation results show that the SCR protocol achieves a considerably higher performance at Bob than at Eve since Eve does not know the coefficient matrices and reference images used at Alice and Relay for the RLNC. The SCR protocol is also shown to outperform the counterpart secure direct transmission protocol without the relaying links and secure relaying transmission without the direct link. Additionally, an increased scaling factor can save the transmission bandwidth for a slight change in the image quality. Moreover, the impacts of direct, relaying and wiretap links are evaluated, verifying the effectiveness of the SCR protocol with the employment of Relay to assist the image communications between Alice and Bob in the WRNs.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] Positron Image Super-Resolution Using Generative Adversarial Networks
    Xiong, Fang
    Liu, Jian
    Zhao, Min
    Yao, Min
    Guo, Ruipeng
    IEEE ACCESS, 2021, 9 : 121329 - 121343
  • [32] Deep Bi-Dense Networks for Image Super-Resolution
    Wang, Yucheng
    Shen, Jialiang
    Zhang, Jian
    2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), 2018, : 404 - 411
  • [33] Single Image Super-Resolution Using Feedback Attention Networks
    Zhang, Juntao
    Dong, Hongbin
    Huang, Ruolin
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 2808 - 2814
  • [34] Channel Attention Network for Wireless Capsule Endoscopy Image Super-Resolution
    Sarvaiya, Anjali
    Vaghela, Hiren
    Upla, Kishor
    Raja, Kiran
    Pedersen, Marius
    COMPUTER VISION AND IMAGE PROCESSING, CVIP 2023, PT II, 2024, 2010 : 432 - 444
  • [35] Frequency Separation Network for Image Super-Resolution
    Li, Shanshan
    Cai, Qiang
    Li, Haisheng
    Cao, Jian
    Wang, Lei
    Li, Zhuangzi
    IEEE ACCESS, 2020, 8 : 33768 - 33777
  • [36] Deep Learning for Image Super-Resolution: A Survey
    Wang, Zhihao
    Chen, Jian
    Hoi, Steven C. H.
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (10) : 3365 - 3387
  • [37] Blind Image Super-Resolution: A Survey and Beyond
    Liu, Anran
    Liu, Yihao
    Gu, Jinjin
    Qiao, Yu
    Dong, Chao
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 45 (05) : 5461 - 5480
  • [38] Multiple improved residual networks for medical image super-resolution
    Qiu, Defu
    Zheng, Lixin
    Zhu, Jianqing
    Huang, Detian
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2021, 116 : 200 - 208
  • [39] Deep Convolutional Networks-Based Image Super-Resolution
    Lin, Guimin
    Wu, Qingxiang
    Huang, Xixian
    Qiu, Lida
    Chen, Xiyao
    INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT I, 2017, 10361 : 338 - 344
  • [40] Reivew of Light Field Image Super-Resolution
    Yu, Li
    Ma, Yunpeng
    Hong, Song
    Chen, Ke
    ELECTRONICS, 2022, 11 (12)