A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography

TL;DR

This paper introduces a novel iterative image reconstruction algorithm for electrical capacitance tomography that combines total variation regularization with adaptive reweighted compressive sensing to improve image sharpness and accuracy.

Contribution

The proposed algorithm uniquely integrates adaptive weighting with total variation and addresses non-linear effects, enhancing ECT image reconstruction over existing methods.

Findings

More precise ECT image recovery compared to state-of-the-art algorithms

Effective in imaging multiphase systems in industrial and medical applications

Reduces non-linear effects through an updated sensitivity matrix

Abstract

A new iterative image reconstruction algorithm for electrical capacitance tomography (ECT) is proposed that is based on iterative soft thresholding of a total variation penalty and adaptive reweighted compressive sensing. This algorithm encourages sharp changes in the ECT image and overcomes the disadvantage of the $l_1$ minimization by equipping the total variation with an adaptive weighting depending on the reconstructed image. Moreover, the non-linear effect is also partially reduced due to the adoption of an updated sensitivity matrix. Simulation results show that the proposed algorithm recovers ECT images more precisely than existing state-of-the-art algorithms and therefore is suitable for the imaging of multiphase systems in industrial or medical applications.