Optical machine learning with incoherent light and a single-pixel detector.

Abstract

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 work only under coherent light illumination, and the precision requirement in practical experiments is quite high. This Letter 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.