基于深度学习的相位调制全息数据存储的图像分割

IF 3.2 2区 物理与天体物理 Q2 OPTICS
Optics express Pub Date : 2024-09-09 DOI:10.1364/oe.536783
Ruixian Chen, Jinyu Wang, Shaodong Zhang, Rongquan Fan, Dakui Lin, Xiong Li, Jihong Zheng, Qiang Cao, Jianying Hao, Xiao Lin, Xiaodi Tan
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引用次数: 0

摘要

基于数据驱动深度学习(DL)的相位检索是一种适用于相位调制全息数据存储(HDS)的解码方法。一旦 DL 网络训练完成,就可以直接从相应的衍射强度图像中检索相位,而且数据传输率高、误码率低。传统的基于数据驱动的衍射网络相位检索需要大量已知样本进行训练,这对于像 HDS 这样的实际应用来说通常非常费力。本文提出了一种基于图像特征的图像分割方法,可将用于训练 DL 网络的原始样本对(OSP)数量减少约 54 倍。所提出的方法在 HDS 的实际应用中很容易实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image segmentation of phase-modulated holographic data storage based on deep learning
Phase retrieval based on data-driven deep learning (DL) is a suitable decoding method for phase-modulated holographic data storage (HDS). Once the DL network is trained, the phase can be directly retrieved from the corresponding diffraction intensity image with high data transfer rate and low bit error rate. Traditional data-driven DL-based phase retrieval requires a large number of known samples for training, which is usually laborious for practical applications such as HDS. In the paper, we propose an image segmentation method based on image features, leading to about 54 times reduction in the number of original sample pairs (OSP) for training DL network. The proposed method is easy to implement in practical situations of HDS.
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来源期刊
Optics express
Optics express 物理-光学
CiteScore
6.60
自引率
15.80%
发文量
5182
审稿时长
2.1 months
期刊介绍: Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.
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