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

被引：0

作者：

Wang, Yu ^{[1
]}

Wang, Xiaoqian ^{[1
]}

Gao, Chao ^{[1
]}

Yu, Zhuo ^{[1
,2
]}

Wang, Hong ^{[1
]}

Zhao, Huan ^{[1
]}

Yao, Zhihai ^{[1
]}

机构：

[1] Changchun Univ Sci & Technol, Dept Phys, Changchun 130022, Peoples R China

[2] Baicheng Normal Univ, Sch Phys & Elect, Baicheng 137000, Peoples R China

来源：

SENSORS | 2024年 / 24卷 / 13期

关键词：

deep learning; self supervised; neural networks; ghost imaging; DEEP-LEARNING APPROACH; QUALITY ASSESSMENT; SCATTERING MEDIA;

D O I：

10.3390/s24134197

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：10

共 5 条

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