  We present a hybrid image classifier by mode-selective image upconversion,
single pixel photodetection, and deep learning, aiming at fast processing a
large number of pixels. It utilizes partial Fourier transform to extract the
signature features of images in both the original and Fourier domains, thereby
significantly increasing the classification accuracy and robustness. Tested on
the MNIST handwritten digit images, it boosts the accuracy from 81.25% to
99.23%, and achieves an 83% accuracy for highly contaminated images whose
signal-to-noise ratio is only -17 dB. Our approach could prove useful for fast
lidar data processing, high resolution image recognition, occluded target
identification, atmosphere monitoring, and so on.
