A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography
This addresses image reconstruction for multiphase systems in industrial or medical applications, representing an incremental improvement over existing methods.
The authors tackled the problem of electrical capacitance tomography (ECT) image reconstruction by proposing a new iterative algorithm combining total variation penalty with adaptive reweighted compressive sensing. Simulation results showed it recovers ECT images more precisely than existing state-of-the-art algorithms.
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.