A novel computational imaging algorithm based on split Bregman iterative for electrical capacitance tomography

2021 ◽  
Author(s):  
Qing Zhao ◽  
Shi Liu ◽  
Xinxin Chai ◽  
Hongbo Guo
Keyword(s):  
Computational Imaging ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Split Bregman ◽  
Imaging Algorithm ◽  
Capacitance Tomography

scholarly journals Image Reconstruction Method with the Exploitation of the Spatial Correlation for Electrical Capacitance Tomography

2015 ◽  
Vol 15 (6) ◽  
pp. 284-293 ◽  
Author(s):  
Jing Lei ◽  
Shi Liu
Keyword(s):  
Spatial Correlation ◽  
Total Variation ◽  
Dynamic Imaging ◽  
Measurement Information ◽  
Electrical Capacitance Tomography ◽  
Reconstruction Method ◽  
Electrical Capacitance ◽  
Split Bregman ◽  
Capacitance Tomography ◽  
Objective Functional

Abstract Electrical capacitance tomography (ECT) is considered to be a competitive measurement method. The imaging objects in ECT measurements are often in a time-varying process, and exploiting the prior information related to the dynamic nature is important for reconstructing high-quality images. Different from existing reconstruction models, in this paper a new model that incorporates the spatial correlation of the pixels by introducing the radial basis function (RBF) method, the dynamic behaviors of a timevarying imaging object, and the ECT measurement information is proposed to formulate the dynamic imaging problem. An objective functional that exploits the spatial correlation of the pixels, the combinational regularizer of the first-order total variation (FOTV) and the second-order total variation (SOTV), the multi-scale regularization, the spatial constraint, and the temporal correlation is proposed to convert the ECT imaging task into an optimization problem. A split Bregman iteration (SBI) method based iteration scheme is developed for solving the proposed objective functional. Numerical simulation results validate the superiority of the proposed reconstruction method on the improvement of the imaging quality.

scholarly journals Electrical Capacitance Tomography Measurement of the Migration of Ice Frontal Surface in Freezing Soil

2016 ◽  
Vol 16 (6) ◽  
pp. 280-286 ◽  
Author(s):  
J. Liu ◽  
X.M. Suo ◽  
S.S. Zhou ◽  
S.Q. Meng ◽  
S.S. Chen ◽  
...  
Keyword(s):  
Loss Function ◽  
Optimal Solution ◽  
Frozen Soil ◽  
Freezing Process ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Split Bregman ◽  
Frontal Surface ◽  
Freezing Soil ◽  
Capacitance Tomography

AbstractThe tracking of the migration of ice frontal surface is crucial for the understanding of the underlying physical mechanisms in freezing soil. Owing to the distinct advantages, including non-invasive sensing, high safety, low cost and high data acquisition speed, the electrical capacitance tomography (ECT) is considered to be a promising visualization measurement method. In this paper, the ECT method is used to visualize the migration of ice frontal surface in freezing soil. With the main motivation of the improvement of imaging quality, a loss function with multiple regularizers that incorporate the prior formation related to the imaging objects is proposed to cast the ECT image reconstruction task into an optimization problem. An iteration scheme that integrates the superiority of the split Bregman iteration (SBI) method is developed for searching for the optimal solution of the proposed loss function. An unclosed electrodes sensor is designed for satisfying the requirements of practical measurements. An experimental system of one dimensional freezing in frozen soil is constructed, and the ice frontal surface migration in the freezing process of the wet soil sample containing five percent of moisture is measured. The visualization measurement results validate the feasibility and effectiveness of the ECT visualization method

A NEW IMAGING ALGORITHM FOR ELECTRIC CAPACITANCE TOMOGRAPHY

2016 ◽  
Vol 63 (0) ◽  
pp. 27-41 ◽  
Author(s):  
Maciej PAŃCZYK ◽  
Jan SIKORA
Keyword(s):  
Sensitivity Analysis ◽  
Final Section ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Imaging Algorithm ◽  
Advantages And Disadvantages ◽  
Electric Capacitance ◽  
Adjoint Variables ◽  
Capacitance Tomography

A new imaging algorithm of permittivity for Electrical Capacitance Tomography (ECT) was proposed in this paper. Some aspects of sensitivity analysis and the application of adjoint variables were discussed. In the final section of this paper some advantages and disadvantages were shown.

Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography

2018 ◽  
Vol 41 (9) ◽  
pp. 2389-2399 ◽  
Author(s):  
Lian Lu ◽  
Guowei Tong ◽  
Ge Guo ◽  
Shi Liu
Keyword(s):  
Cost Function ◽  
Tikhonov Regularization ◽  
Measurement Data ◽  
Reconstruction Algorithm ◽  
Electrical Capacitance Tomography ◽  
Reconstruction Technique ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Split Bregman ◽  
Capacitance Tomography

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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