A New Algorithm for Image Reconstruction of Electrical Capacitance Tomography Based on Inverse Heat Conduction Problems

Haddadi, Mohammad B.; Maddahian, Reza

doi:10.1109/jsen.2015.2506409

Cited by 16 publications

(10 citation statements)

References 44 publications

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“…In the ECT model, the nonlinear effects can be resolved in two aspects. The first one is the approximation of the linear model in (5). The second non-linear effect lies on the accuracy of the measurements of the sensitivity matrix S. Two techniques are introduced here to address the non-linear effect, respectively [30], [31].…”

Section: Reweighted Tv-ist Algorithm For Non-linear Ectmentioning

confidence: 99%

A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography

Chandrasekera

et al. 2018

IEEE Sensors J.

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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 l1 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. Index Terms-Electrical capacitance tomography (ECT), iterative reconstruction, reweighted total variation, non-linear effect.

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Section: Reweighted Tv-ist Algorithm For Non-linear Ectmentioning

confidence: 99%

A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography

Chandrasekera

et al. 2018

IEEE Sensors J.

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“…The ECT technique uses the measured capacitance data to reconstruct the permittivity distribution in a specific measurement area. The capacitance produced by electrode pairs is governed by the Poisson equation (Haddadi and Maddahian, 2016)…”

Section: Ect Modelmentioning

confidence: 99%

Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography

Tong

Guo

et al. 2018

Transactions of the Institute of Measurement and Control

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The electrical capacitance tomography (ECT) technique uses the measured capacitance data to reconstruct the permittivity distribution in a specific measurement area, in which the performances of reconstruction algorithms play a crucial role in the reliability of measurement results. According to the Tikhonov regularization technique, a new cost function with the total least squares technique and the ℓ1-norm based regularizer is presented, in which measurement noises, model deviations and the influence of the outliers in the measurement data are simultaneously considered. The split Bregman technique and the fast-iterative shrinkage-thresholding method are combined into a new iterative scheme to solve the proposed cost function efficiently. Numerical experiment results show that the proposed algorithm achieves the boost in the precision of reconstruction, and under the noise-free condition the image errors for the imaging targets simulated in this paper, that is, 8.4%, 12.4%, 13.5% and 6.4%, are smaller than the linear backprojection (LBP) algorithm, the Tikhonov regularization (TR) algorithm, the truncated singular value decomposition (TSVD) algorithm, the Landweber algorithm and the algebraic reconstruction technique (ART).

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“…This process is carried out by the variations in the distribution and/or condensation of dielectric materials inside the vessels. The outcome is the reconstruction of cross-sectional images representing the permittivity distribution obtained from the measured data [27].…”

Section: Introductionmentioning

confidence: 99%

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

Deabes

Bouazza

2021

IEEE Access

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One of the vital processes that should be monitored and analyzed continuously in the oilgas and petroleum-related industries is the multi-phase flow inside pipes. Multi-phase flow means flowing two or more phases of gas, liquid, or solid inside a pipe. Electrical Capacitance Tomography (ECT) is a feasible and economical solution for monitoring dynamic applications. The ECT system offers the benefits of no radiation, non-intrusive, and non-invasive. Despite its potential, ECT systems deployment's major limitation is the crucial need to develop rapid image reconstruction algorithms. In this paper, a Local Ensemble Transform Kalman Filter (LETKF) is developed as a non-linear system estimator for reconstructing images in the ECT system. This method manages each node of the model independently by assimilating only the observations at a predefined distance. The localized approach of the LETKF gives it high computational efficiency allowing it to be applied to large dynamic systems. A quantitative analysis using Image Error (IE) and Coefficient Correlation (CC) measures has been applied to prove the effectiveness of the proposed algorithm. Indeed, the IE has been significantly decreased (around 62%), and the CC greatly increased (around 58%). Then, the influence of the noise was discussed. The results are promising and prove the algorithm feasibility.INDEX TERMS ECT, image reconstruction, Kalman filter, multi-phase flow.

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A New Algorithm for Image Reconstruction of Electrical Capacitance Tomography Based on Inverse Heat Conduction Problems

Cited by 16 publications

References 44 publications

A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography

A Non-Linear Reweighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography

Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography

Efficient Image Reconstruction Algorithm for ECT System Using Local Ensemble Transform Kalman Filter

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