Hybrid Iterative Reconstruction Method for Imaging Problems in ECT

As a powerful visualization tomography technique, electrical capacitance tomography (ECT) method, which reconstructs the permittivity distribution in a sensing domain according to the measured capacitance data, has been widely used in different industry scenarios. However, the image reconstruction in ECT is an ill-posed problem, and its application is plagued by low-quality reconstructions, and seeking for a robust algorithm to reduce reconstruction errors and artifacts is crucial. To increase the reconstruction quality, a novel cost function is built for imaging, in which the L1-2 norm is designed as a regularizer for encoding the sparsity prior of imaging objects. The ant lion optimizer (ALO) algorithm and the alternating direction method of multipliers (ADMMs) with the Douglas-Rachford splitting (DRS) method and the soft thresholding algorithm as powerful optimizers for minimizing the subproblems are combined into a novel solver for solving the built cost function more effectively. Simulation and experimental results indicate that the proposed imaging technique can improve the quality of reconstructed images.