  An optical diffractive neural network (DNN) can be implemented with a
cascaded phase mask architecture. Like an optical computer, the system can
perform machine learning tasks such as number digit recognition in an
all-optical manner. However, the system can only work under coherent light
illumination and the precision requirement in practical experiments is quite
high. This paper proposes an optical machine learning framework based on
single-pixel imaging (MLSPI). The MLSPI system can perform the same linear
pattern recognition task as DNN. Furthermore, it can work under incoherent
lighting conditions, has lower experimental complexity and can be easily
programmable.
