Complex-valued convolutional neural network for terahertz image classification

Abstract

Complex-valued convolutional neural networks (CVCNN) have better performance than real-valued neural networks in the field of terahertz imaging. In this paper, a complex-valued neural network is innovatively applied to terahertz image classification task in a vector network analyzer (VNA) imaging system. The complex-valued CNN (CVCNN) processing framework for terahertz image classification is proposed. Terahertz image datasets are constructed using MINIST handwritten datasets and PSF which was measured from our transmission system. Compared to CNN, CVCNN has a better accuracy rate, and it is significantly less vulnerable to over-fitting. Phase information can be used well at the same time, which is impossible for the CNN. The method of training data generation is given, and some specific implementation details are given. the superiority of the method in this paper is verified by using simulated and measured data obtained from 200Ghz image system.