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Laser & Optoelectronics Progress, Volume. 57, Issue 2, 21014(2020)

Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network

Liu Kewen1,2, Ma Yuan1,2, Xiong Hongxia3、*, Yan Zejun4, Zhou Zhijun5, Liu Chaoyang6, Fang Panpan1,2, Li Xiaojun1,2, and Chen Yalei1,2
Author Affiliations
  • 1School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 2Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 3School of Civil Engineering & Architecture, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 4Department of Urology, Ningbo First Hospital, Key Laboratory of Translational Medicine of Urological Diseases in Ningbo, Ningbo, Zhejiang 315010, China
  • 5Department of Urology, the First People''s Hospital of Tianmen, Tianmen, Hubei 431700, China
  • 6State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China
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    Article index updated: Sep. 11, 2025

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    Liu Kewen, Ma Yuan, Xiong Hongxia, Yan Zejun, Zhou Zhijun, Liu Chaoyang, Fang Panpan, Li Xiaojun, Chen Yalei. Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21014

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    Paper Information

    Category: Image Processing

    Received: Jun. 4, 2019

    Accepted: --

    Published Online: Jan. 3, 2020

    The Author Email: Xiong Hongxia (xionghongxia@whut.edu.cn)

    DOI:10.3788/LOP57.021014

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology