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Height quantized diffractive deep neural networks

delete2025-02-26
delete0
PRE
AI
R
Runze Li
X
Xuhui Zhuang
D
Ding, Gege
M
Mingzhu Song
G
Guang Jin
X
Xuemin Zhang
J
Jie Wen
S
Shaoju Wang *
DOI：10.1088/1402-4896/adb651delete
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摘要

摘要

En 中文
衍射深度神经网络（D2NN）在人工智能图像处理任务中，如无透镜成像，已展现出优势。由于当前制造技术（如光刻和3D打印）的精度和分辨率难以满足低成本D2NN制造的要求，为了解决这一问题，我们设计了高度量化的衍射深度神经网络，从而提高了网络的细节分辨率并降低了制造精度要求。我们实验验证了所提出网络的性能，仿真结果表明该结构能在更短的训练时间内达到相同的训练效果。此外，将量化过程引入D2NN作为一种噪声，能够部分避免D2NN的过拟合。
Keyword:
diffractive deep neural networks
computational imaging
diffractive imaging

期刊

Physica Scripta 封面图
Physica Scripta
IF:
2.6
论文数:
4.3K
被引数:
2.5W

机构

C
china waterborne transport res inst
学者数:
27
论文数: 12
被引数: 2
引用论文

引用论文

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err14
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