An INN-Based Solution for the Underdetermined Image Reconstruction Problem in EIT

Abstract

Electrical impedance tomography (EIT) has been widely concerned in online nondestructive testing of carbon fiber-reinforced polymers (CFRPs) due to its significant advantages of high real-time performance, zero radiation, simple structure, and low cost. However, its inverse problem is highly underdetermined and nonlinear, thus limiting its imaging quality and application scope. Based on the architecture of an invertible neural network (INN), an INN image reconstruction algorithm with latent output variables is proposed in this article. With latent output variables, the information lost during training is reused, and a unique input can be determined by sampling latent output variables and predicted outputs. In addition, the forward and reverse processes are combined together to weaken the dependency of effective output and latent output variables, reduce the errors between input and output domains, and solve the underdetermination and nonlinearity problems of EIT. The simulation and experimental results showed that compared with the traditional algorithms and the convolution neural network (CNN) network algorithm, the INN can accurately reconstruct the damage location, yield a clearer damaged edge, and reduce artifacts. Noise-added simulations and prototype experiments showed that the INN achieves both good imaging quality and enhanced noise immunity, to improve the performance of the EIT system in CFRP laminate inspection.