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摘要:
We propose ghost imaging(GI)with deep learning to improve detection speed.GI,which uses an illumination light with random patterns and a single-pixel detector,is correlation-based and thus suitable for detecting weak light.However,its detection time is too long for practical inspection.To overcome this problem,we applied a convolutional neural network that was constructed based on a classification of the causes of ghost image degradation.A feasibility experiment showed that when using a digital mirror device projector and a photodiode,the proposed method improved the quality of ghost images.
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篇名 Ghost Imaging with Deep Learning for Position Mapping of Weakly Scattered Light Source
来源期刊 纳米制造与计量(英文) 学科 工学
关键词 Inspection METROLOGY Defect mapping Ghost imaging Single-pixel imaging Deep learning Weak light imaging
年,卷(期) 2021,(1) 所属期刊栏目
研究方向 页码范围 37-45
页数 9页 分类号 TP3
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
Inspection
METROLOGY
Defect
mapping
Ghost
imaging
Single-pixel
imaging
Deep
learning
Weak
light
imaging
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
纳米制造与计量(英文)
季刊
2520-811X
12-1463/TB
出版文献量(篇)
30
总下载数(次)
0
总被引数(次)
0
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