Color ghost imaging through a dynamic scattering medium based on deep learning

Zhan Yu; Luozhi Zhang; Sheng YuanXing BaiYujie WangXingyu ChenMingze SunXinjia LiYang LiuXin Zhou

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Color ghost imaging through a dynamic scattering medium based on deep learning

机译：Color ghost imaging through a dynamic scattering medium based on deep learning

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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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来源
《Optical Engineering》 |2023年第2期|021005-1-021005-10|共10页
作者
Zhan Yu; Luozhi Zhang; Sheng YuanXing BaiYujie WangXingyu ChenMingze SunXinjia LiYang LiuXin Zhou;
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作者单位

Beihang Univ. (China);

North China Univ. of Water Resources and Electric Power (China);

Sichuan Univ. (China);

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
Scattering; Education and training; Gallium nitride; Image restoration; Deep learning; Color imaging; Signal to noise ratio;

Color ghost imaging through a dynamic scattering medium based on deep learning

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