Alphabetic Image Recognition Based on Multi-channel Diffractive Deep Neural Network

Abstract

Machine learning has made remarkable achievements in image classification in recent years. It has also inspired the emergence of optical machine learning frameworks as an effective combination of optics and artificial intelligence. Diffractive deep neural network (D2NN) is a recently proposed optical neural network framework, which is designed and trained by computers, and then fabricated by the usage of 3D printing technology. It can recognize handwritten digital dataset and fashion product dataset with only light source, and the recognition effect is comparable to that of electronic neural network. We propose a diffractive light neural network structure is improved on the basis of the traditional diffractive deep neural network (D2NN), providing a multi-channel design model, while the distribution of neuron nodes on the network layer is adjusted in such a way that different network layers correspond to incoming light waves of different frequencies. The neuron nodes on the network layer are arranged in a concentric circle pattern to reduce the parameters generated during network training, and in the output layer we get channels of different frequencies and then merge these channels to obtain the final results. It is experimentally verified that the diffractive optical neural network with multiple channels has a large improvement of 95.44% recognition accuracy compared to the 89.92% recognition accuracy of the traditional D2NN for recognition of 10 different alphabetic image datasets. The proposed network improves the recognition accuracy by increasing the number of channels while reducing the parameters, which provides a guidance to design optical networks with reduced cost and improved efficiency.