A Regularization-Guided Deep Imaging Method for Electrical Impedance Tomography

Abstract

Electrical Impedance Tomography (EIT) is a promising technique for the process monitoring. However, due to the inherent nonlinearity and ill- posedness of the image reconstruction in EIT, it remains a great challenge for EIT application. A regularization-guided deep imaging (RGDI) method which combines the mathematical structure of the regularization reconstruction method with deep learning is proposed. As a ‘gray-box’, RGDI can not only solve the problem of parameter selection in regularization algorithms, but also depend less on the complete database. The structure of RGDI is composed of three sequentially connected modules, i.e. data pre-processing, feature extraction and image reconstruction. The feature extraction module contains five blocks, and each block consists of a regularization network and a convolutional neural network (CNN) with the function of an ‘encoding-decoding’. To obtain more accurate reconstruction images, a two-layer fully connected neural network (FCNN) is applied. During the training of RGDI network, prior information of structural features is added to avoid the overfitting problem and accelerate its convergence. The experimental results illustrate that the image quality of RGDI is better than that of numerical imaging methods and several reported deep learning methods.