Fuzzy Optimization in Electrical Tomography Imaging Based on Expectation-Maximization Algorithm

Electrical Tomography (ET) is an advanced visualization technique with low-cost, non-invasiveness, nonpolluting, and fast-response advantages. However, inherent illposed problems and soft-field effects in the ET reconstruction process cause uncertainty in boundary measurements, thereby decreasing the quality of ET reconstruction. To solve this problem, this study employs Gaussian-type fuzzy membership functions to represent measurement uncertainty. The study constructs the objective function for ET reconstruction and performs fuzzy optimization using the Expectation-Maximization (EM) algorithm, aiming to enhance the accuracy and stability of ET reconstruction. Experimental findings affirm the effectiveness of the proposed approach in improving the ET reconstruction quality, offering a novel and valuable tool for ET reconstruction.