10 December 2022 Color ghost imaging through a dynamic scattering medium based on deep learning
Zhan Yu, Luozhi Zhang, Sheng Yuan, Xing Bai, Yujie Wang, Xingyu Chen, Mingze Sun, Xinjia Li, Yang Liu, Xin Zhou
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Abstract

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.

© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
Zhan Yu, Luozhi Zhang, Sheng Yuan, Xing Bai, Yujie Wang, Xingyu Chen, Mingze Sun, Xinjia Li, Yang Liu, and Xin Zhou "Color ghost imaging through a dynamic scattering medium based on deep learning," Optical Engineering 62(2), 021005 (10 December 2022). https://doi.org/10.1117/1.OE.62.2.021005
Received: 22 July 2022; Accepted: 29 November 2022; Published: 10 December 2022
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Cited by 1 scholarly publication.
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KEYWORDS
Scattering

Education and training

Gallium nitride

Image restoration

Deep learning

Color imaging

Signal to noise ratio

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