灰度
计算机科学
奈奎斯特频率
人工智能
噪音(视频)
人工神经网络
固定模式噪声
对象(语法)
鬼影成像
奈奎斯特-香农抽样定理
计算机视觉
像素
模式识别(心理学)
图像(数学)
滤波器(信号处理)
作者
Jianing Cao,Yu-Hui Zuo,Huahua Wang,Weidong Feng,Zhi-Xin Yang,Jian Ma,Haoran Du,Lu Gao,Ze Zhang
出处
期刊:Applied Optics
[Optica Publishing Group]
日期:2021-09-16
卷期号:60 (29): 9180-9180
被引量:15
摘要
A single-pixel neural network object classification scenario in the sub-Nyquist ghost imaging system is proposed. Based on the neural network, objects are classified directly by bucket measurements without reconstructing images. Classification accuracy can still be maintained at 94.23% even with only 16 measurements (less than the Nyquist limit of 1.56%). A parallel computing scheme is applied in data processing to reduce the object acquisition time significantly. Random patterns are used as illumination patterns to illuminate objects. The proposed method performs much better than existing methods for both binary and grayscale images in the sub-Nyquist condition, which is also robust to environment noise turbulence. Benefiting from advantages of ghost imaging, it may find applications for target recognition in the fields of remote sensing, military defense, and so on.
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