Optical Fourier convolutional neural network with high efficiency in image classification
Yan Liu, Jianwei Qin, Yanbing Liu, Yuanan Liu, Xun Liu, Fangwei Ye, Wei Li
Abstract
Compared to traditional neural networks, optical neural networks demonstrate significant advantages in terms of information processing speed, energy efficiency, anti-interference capability, and scalability. Despite the rapid development of optical neural networks in recent years, most existing systems still face challenges such as complex structures, time-consuming training, and insufficient accuracy. This study fully leverages the coherence of optical systems and introduces an optical Fourier convolutional neural network based on the diffraction of complex image light fields. This new network is not only structurally simple and fast in computation but also excels in image classification accuracy. Our research opens new perspectives for the development of optical neural networks, and also offers insights for future applications in high-efficiency, low-energy-consumption computing domains.