Supervised Descent Method Using Neural Networks for 2D Electrical Impedance Tomography

Abstract

In this work, we study the application of the supervised descent method using neural networks (SDMNN) for 2D electrical impedance tomography. SDMNN contains the offline training and the online prediction stages. In the offline stage, neural networks are iteratively applied to learn a set of descent directions for minimizing the objective function; in the online stage, the trained neural networks can be directly used for the image reconstruction. This scheme combines the advantages of fast convergence speed of neural networks and good generalization ability of the supervised descent method (SDM). Numerical results verify the efficiency and accuracy of this method.