Object identification in computational ghost imaging based on deep learning

被引：14

作者：

Li, Jianbo ^{[1
]}

Le, Mingnan ^{[1
]}

Wang, Jun ^{[1
]}

Zhang, Wei ^{[1
]}

Li, Bin ^{[1
]}

Peng, Jinye ^{[1
]}

机构：

[1] Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China

来源：

APPLIED PHYSICS B-LASERS AND OPTICS | 2020年 / 126卷 / 10期

关键词：

Processing - Hadamard matrices - Deep neural networks;

D O I：

10.1007/s00340-020-07514-4

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Processing method plays an important role in accelerating imaging process in ghost imaging. In this study, we propose a processing method with the Hadamard matrix and a deep neural network called ghost imaging hadamard neural network (GIHNN). We focus on how to break through the bottleneck of image reconstruction time, and GIHNN can identify an object before the imaging process. Our research reveals that the light intensity value contains the feature information of the object and expands the possibility of further applications of artificial intellectual techniques in computational ghost imaging.

引用

页数：10

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