Aiming at the problem of low quality in image reconstruction of traditional image reconstruction algorithm of electromagnetic tomography(EMT), an EMT image reconstruction algorithm based on autoencoder neural network of Restricted Boltzmann Machine (RBM) is proposed. Firstly, the basic principles of EMT system and autoencoder neural network are analyzed. Autoencoder neural network is a deep learning model, which contains two parts: encoder and decoder. The encoding process of the encoder is equivalent to the object field detection process in the EMT system; the decoding process of the decoder is equivalent to the image reconstruction process. On this basis, an autoencoder neural network model is built. In this model, the RBM is used for layer by layer pre-training to obtain the initial weight and offset, and the global weight and offset are adjusted by BP algorithm. The parameter file generated in the trained autoencoder neural network is used to construct a decoder. Finally, the detected voltage value output by the EMT system is input into the decoder network to obtain the reconstructed image of the EMT. Furthermore, data with Gaussian noise and data regarding flow pattern not in training dataset are used to test the generalization ability and practicability of the network, respectively. The experimental results show that the method in this paper is a kind of EMT image reconstruction method with higher accuracy, which also provides a new means for EMT image reconstruction.

针对传统电磁断层扫描(EMT)图像重建算法图像重建质量低的问题，提出了一种基于受限玻尔兹曼机(RBM)自编码神经网络的EMT图像重建算法。首先分析了EMT系统和自编码神经网络的基本原理。Autoencoder 神经网络是一种深度学习模型，它包含两部分：编码器和解码器。编码器的编码过程相当于EMT系统中的物体场检测过程；解码器的解码过程相当于图像重建过程。在此基础上，建立了自编码器神经网络模型。在这个模型中，RBM用于逐层预训练，得到初始权重和偏移量，并通过BP算法调整全局权重和偏移量。在训练好的自动编码器神经网络中生成的参数文件用于构建解码器。最后将EMT系统输出的检测电压值输入解码器网络，得到EMT的重构图像。此外，使用高斯噪声数据和不在训练数据集中的流型数据分别测试网络的泛化能力和实用性。实验结果表明，本文方法是一种精度更高的EMT图像重建方法，也为EMT图像重建提供了一种新的手段。EMT系统输出的检测电压值输入解码器网络，得到EMT的重构图像。此外，使用高斯噪声数据和不在训练数据集中的流型数据分别测试网络的泛化能力和实用性。实验结果表明，本文方法是一种精度更高的EMT图像重建方法，也为EMT图像重建提供了一种新的手段。EMT系统输出的检测电压值输入解码器网络，得到EMT的重构图像。此外，使用高斯噪声数据和不在训练数据集中的流型数据分别测试网络的泛化能力和实用性。实验结果表明，本文方法是一种精度更高的EMT图像重建方法，也为EMT图像重建提供了一种新的手段。