物理
计算机科学
人工智能
一般化
深度学习
卷积神经网络
分割
滤波器(信号处理)
干涉测量
集合(抽象数据类型)
数据集
相(物质)
计算机视觉
模式识别(心理学)
光学
数学
数学分析
有机化学
化学
程序设计语言
作者
Le Zou,Tian-Ci Zheng,Xing Yang,Haiming Zhang,Xiaoyan Li,Jia Ren,Da-Bin Lin,En‐Wei Liang
出处
期刊:Applied Optics
[The Optical Society]
日期:2020-12-21
卷期号:60 (1): 10-10
被引量:7
摘要
This paper proposes an unwrapping algorithm based on deep learning for inertial confinement fusion (ICF) target interferograms. With a deep convolutional neural network (CNN), the task of phase unwrapping is transferred into a problem of semantic segmentation. A method for producing the data set for the ICF target measurement system is demonstrated. The noisy wrapped phase is preprocessed using a guided filter. Postprocessing is introduced to refine the final result, ensuring the proposed method can still accurately unwrap the phase even when the segmentation result of the CNN is not perfect. Simulations and actual interferograms show that our method has better accuracy and antinoise ability than some classical unwrapping approaches. In addition, the generalization capability of our method is verified by successfully applying it to an aspheric nonnull test system. By adjusting the data set, the proposed method may be transferred to other systems.
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