基于深度学习的全息存储相位检索抗噪声研究

Jianying Hao, Yongkun Lin, Mingyong Chen, Xiao Lin, X. Tan, Yuhong Ren
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摘要

相位恢复是相位调制全息存储的关键技术。本文提出了一种基于深度卷积神经网络的相位数据直接检索方法。与传统的非干涉相位恢复方法相比,该方法具有恢复速度快、重建精度高等优点。本文研究和分析了不同检索条件下图像噪声强度对检索结果的影响。通过建立与实际实验严格符合的仿真系统,生成了无透镜空间衍射图像。通过在强度图像中加入不同比例的随机噪声,得到训练数据集。卷积神经网络通过训练数据集进行训练,并通过新的带噪测试数据集进行测试。实验结果表明,基于深度学习的相位检索方法具有较高的系统误差容忍度和较强的抗噪声性能。
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Research on anti-noise of deep learning-based phase retrieval in holographic storage
Phase retrieval is the key technique in phase-modulated holographic storage. In this paper, a deep convolutional neural network is proposed to directly retrieve phase data. Compared with the traditional non-interferometric phase retrieval method, this method has the advantages of fast retrieval speed and high reconstruction accuracy. In this paper, the influence of intensity image noise on retrieval results under different retrieved conditions is researched and analyzed. By establishing a simulation system that is in strict agreement with real experiments, the lensless spatial diffraction images are generated. By adding different proportions of random noise into the intensity images we get the training dataset. The convolutional neural network is trained by a training dataset and tested by a new noisy test dataset. Experimental results show that the phase retrieval method based on deep learning has a high tolerance for systematic errors and strong anti-noise performance.
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