Single-pixel neural network object classification of sub-Nyquist ghost imaging
Jianing Cao, Yu-Hui Zuo, Huahua Wang, Weidong Feng, Zhi-Xin Yang, Jian Ma, Haoran Du, Lu Gao, Ze Zhang
Abstract
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.