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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