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Deep-learning-based binary hologram

机译:基于深度学习的二进制全息图

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摘要

Binary hologram generation based on deep learning is proposed. The proposed method can reduce the severe effect of quality degradation from binarizing gray-scaled holograms by optimizing the neural network to output binary amplitude holograms directly. In previous work on binary holograms, the calculation time for generating binary holograms was long. However, in the proposed method, once the neural network is trained enough, the neural network generates binary holograms much faster than previous work with comparable quality. The proposed method is more suitable for opportunities to generate several binary holograms under the same condition. The feasibility of the proposed method was confirmed experimentally. (C) 2020 Optical Society of America.
机译:提出了基于深度学习的二进制全息图代。 该方法通过优化神经网络直接输出二进制幅度全息图,可以降低质量劣化的严重效果。 在先前的二进制全息图上的工作中,生成二进制全息图的计算时间很长。 然而,在所提出的方法中,一旦神经网络训练了,神经网络就会比以前的高质量更快地产生二进制全息图。 该方法更适合在相同条件下产生几个二进制全息图的机会。 实验证实了所提出的方法的可行性。 (c)2020美国光学学会。

著录项

  • 来源
    《Applied optics》 |2020年第23期|共6页
  • 作者单位

    Wakayama Univ Grad Sch Syst Engn 930 Sakaedani Wakayama 6408510 Japan;

    Wakayama Univ Grad Sch Syst Engn 930 Sakaedani Wakayama 6408510 Japan;

    Wakayama Univ Fac Syst Engn 930 Sakaedani Wakayama 6408510 Japan;

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  • 正文语种 eng
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