A W-Shaped Self-Supervised Computational Ghost Imaging Restoration Method for Occluded Targets

We developed a novel method based on self-supervised learning to improve the ghost imaging of occluded objects. In particular, we introduced a W-shaped neural network to preprocess the input image and enhance the overall quality and efficiency of the reconstruction method. We verified the superiorit...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 24; no. 13; p. 4197
Main Authors Wang, Yu, Wang, Xiaoqian, Gao, Chao, Yu, Zhuo, Wang, Hong, Zhao, Huan, Yao, Zhihai
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 28.06.2024
Subjects
Deep learning
Efficiency
ghost imaging
Methods
Neural networks
Principles
self supervised
Sensors
ghost imaging
deep learning
self supervised
neural networks
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Summary:We developed a novel method based on self-supervised learning to improve the ghost imaging of occluded objects. In particular, we introduced a W-shaped neural network to preprocess the input image and enhance the overall quality and efficiency of the reconstruction method. We verified the superiority of our W-shaped self-supervised computational ghost imaging (WSCGI) method through numerical simulations and experimental validations. Our results underscore the potential of self-supervised learning in advancing ghost imaging.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24134197