Continuous Real-Time Image Processing by Variable Optical Processor

Diffractive deep neural network (D<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>NN), a type of ONN that processes images using light propagation through free space, has been shown to be capable of performing various image processing tasks, yet it still relies partially on electrical signals. In this report, we achieved completely all-optical and continuous real-time processing of two-dimensional visible information directly using light from real objects without converting the input information into any electrical signals. Firstly, two image processing tasks, classification (MNIST) and detection of industrial parts, were performed as a verification of the capability of our novel D<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>NN optical information processor. Then, high-speed operation was investigated by two further tasks, classification of glass beads and detection of shapes printed on transparent sheets, in which the visible light from the sample was introduced to and processed by the processor without conversion to electrical signal. The results show that our implementation of a D<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>NN processor is capable of image processing on the nano-second order from the appearance of the sample in the observation area.