Deep Autoencoder Imaging Method for Electrical Impedance Tomography
Xiaoyan Chen, Zichen Wang, Xinyu Zhang, Rong Fu, Di Wang, Miao Zhang, Huaxiang Wang
Abstract
Electrical impedance tomography (EIT) is an effective technique for real-time monitoring, visualization, and analysis of industrial process in a noninvasive manner. However, due to the nonlinear and “soft-field” nature of its inverse problem, image reconstruction of EIT is always limited in image resolution and, in particular, the accuracy of identifying object boundaries. In order to solve the above problems, a novel multilayer autoencoder (MLAE) image reconstruction network that consists of a feature extraction module and an image reconstruction module is proposed. In the proposed method, hierarchical structures are applied to increase the forward information flow and the selected appropriate hidden layers can solve the disappearance of the reverse gradient flow. The training process of MLAE containing self-supervised pretraining and supervised fine-tuning can provide better complex nonlinear mapping and improve the model performance. The experimental and analytical results prove that the MLAE image reconstruction method can obtain higher quality images than the typical algorithms and certain methods based on deep learning.