计算机科学
人工智能
散射
探测器
失真(音乐)
计算机视觉
像素
光学
深度学习
噪音(视频)
人工神经网络
数据集
控制重构
图像(数学)
物理
电信
嵌入式系统
放大器
带宽(计算)
作者
Zhan Yu,Luozhi Zhang,Sheng Yuan,Xing Bai,Yujie Wang,Xingyu Chen,Mingze Sun,Xinjia Li,Yang Liu,Xin Zhou
出处
期刊:Optical Engineering
[SPIE - International Society for Optical Engineering]
日期:2022-12-10
卷期号:62 (02)
被引量:1
标识
DOI:10.1117/1.oe.62.2.021005
摘要
This paper presents a color computational ghost imaging scheme through a dynamic scattering medium based on deep learning that uses a sole single-pixel detector and is trained by a simulated data set. Due to the color distortion and noise sources being caused by the scattering medium and detector, a simulation data generation method is proposed accordingly that easily adapts to the actual environment. Adequate simulation data sets allow the trained artificial neural networks to exhibit strong reconfiguration capabilities for optical imaging results. It is worth noting that the network trained by our method can reconstruct better details of the image than the simulation data sets according to the ideal state. Its effectiveness is demonstrated in optical imaging experiments with both rotated double-sided frosted glass and a milk solution used as the dynamic scattering medium.
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