Optical Instruments, Volume. 41, Issue 4, 36(2019)

Research on high-resolution image reconstruction mechanism based on coding-decoding symmetric neural network

XIONG Rui*, ZHANG Leihong, JIANG Zhoujie, WANG Jianqiang, QIN Bangdao, and LAI Chunli
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    Aiming at many current image reconstruction algorithms such as JPEG decompression and compressive sensing reconstruction, there are some problems such as unclear image and low resolution. This paper proposes a high-resolution image reconstruction mechanism based on code-decoding symmetric neural network. Firstly, the image is compressed to obtain a low-resolution image, and then the low-resolution image is used as an input image to encode-decode a symmetric neural network, and the convolutional neural network is used to encode the feature image, and finally the deconvolution neural network is used. Decoding implements detail recovery of the image. The experimental results show that the image reconstructed by the code-decoded symmetric neural network is clearer than the previous low-resolution image, indicating that the resolution of the image is improved.

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    XIONG Rui, ZHANG Leihong, JIANG Zhoujie, WANG Jianqiang, QIN Bangdao, LAI Chunli. Research on high-resolution image reconstruction mechanism based on coding-decoding symmetric neural network[J]. Optical Instruments, 2019, 41(4): 36

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    Paper Information

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    Received: Sep. 10, 2018

    Accepted: --

    Published Online: Nov. 5, 2019

    The Author Email: Rui XIONG (xiongrui2017 usst@sina.com)

    DOI:10.3969/j.issn.1005-5630.2019.04.006

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