Sparsity-inspired image reconstruction for electrical capacitance tomography

Wang, Hongcheng; Fedchenia, Igor; Shishkin, Serge L.; Finn, Alan; Smith, Lance; Colket, Meredith B.

doi:10.1016/j.flowmeasinst.2015.03.001

Cited by 29 publications

(12 citation statements)

References 29 publications

(50 reference statements)

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“…This section begins with revisiting fundamentals of the ADMM. This is then followed by the deduction of our numerical scheme for solving the proposed cost function (8).…”

Section: Solving Methodsmentioning

confidence: 99%

Prior Image Induced Regularization Method for Electrical Capacitance Tomography

2019

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The image reconstruction is a crucial step in the electrical capacitance tomography. This paper presents a new methodology for improving the reconstruction accuracy. The prior image induced regularization from the deep convolutional extreme learning machine (DCELM) is introduced, which is integrated with the domain knowledge related to imaging targets to form a more effective mathematical model for reconstruction. A new numerical scheme is developed to train the DCELM more effectively. The fast iterative shrinkage-thresholding method is embedded into the alternating direction method of multipliers (ADMM) to form a new solver for the proposed imaging model. Extensive validations are implemented to evaluate the proposed imaging method. The numerical results demonstrate that the proposed imaging technique outperforms the state-of-the-art reconstruction methods and produces better reconstructions. INDEX TERMS Prior image induced regularization, image reconstruction, inverse problem, deep convolutional extreme learning machine, iterative imaging method, electrical capacitance tomography. QIBIN LIU received the B.Eng. and M.Sc. degrees in engineering thermophysics from Xi'an Jiaotong University, China, in 2002 and 2005, respectively, and the Ph.D. degree in engineering thermophysics from the Institute of Engineering Thermophysics, Chinese Academy of Sciences, China, in 2008. He is currently a Professor with the Institute of Engineering Thermophysics, Chinese Academy of Sciences, China. He has published more than 80 research papers. His research interests include solar energy utilization technologies, energy system integration, electrical capacitance tomography, inverse problems in engineering and science, and computational fluid dynamics.

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“…This section begins with revisiting fundamentals of the ADMM. This is then followed by the deduction of our numerical scheme for solving the proposed cost function (8).…”

Section: Solving Methodsmentioning

confidence: 99%

Prior Image Induced Regularization Method for Electrical Capacitance Tomography

2019

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“…As discussed above, the inverse reconstruction process [31]- [34] can obtain the distributions of the objective parameters inside the burner based on the measured capacitances by all electrode pairs. The relationship between the distribution of the materials and the measured capacitance is a nonlinear transfer function as shown in Fig.…”

Section: Experimental Ect Methodsmentioning

confidence: 99%

“…The goal of ECT is to acquire the image vector based on the measured capacitance vector. This process is called image reconstruction [31]- [34]. The most common method is called the linear back projection (LBP) as…”

Section: Experimental Ect Methodsmentioning

confidence: 99%

Direct Measurements of Permittivity of Plasma-Assisted Combustion Using Electrical Capacitance Tomography

Chen

Jia

Mao

et al. 2016

IEEE Trans. Plasma Sci.

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The permittivity distribution and the flame development in a closed plasma-assisted combustor were investigated using an electrical capacitance tomography system. The experiments were conducted at a pressure of 1 atm and at different equivalence ratios. I-V curves measured by the Langmuir probe were utilized to estimate the floating potential (−40 to −10 V) and the electron local parameters (T e and n e ) in the plasma-assisted flames. The results showed that the electron concentration reached the maximum at a location close to the plasma injection point where the primary reaction region of the CH 4 -air premixed flame was located. Major species measurement by emission spectrometer combined with the probe measurement revealed that the kinetic enhancement was more significant than the thermal enhancement in the plasma-assisted combustion. Moreover, the dependence of combustion kinetics including electron/ion concentration and temperature on the flame permittivity was verified. The quantitative permittivity data obtained in this study provide a good validation target for modeling of the electric field in plasma-assisted combustion.

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“…In this paper, the gradient projection for spare reconstruction algorithm based on compressed sensing theory (CS-GPSR) is used to solve the model expressed in equation (8) and to realize the image reconstruction of ECT system.…”

Section: Cs-gpsr Algorithm For Ectmentioning

confidence: 99%

“…Ye et al presented a new basis based on which the permittivity distribution to be reconstructed for ECT is naturally sparse in 2015 [7]. With recent development of compressed sensing (CS), several image reconstruction algorithms for ECT based on CS were proposed, in which the sparsity process is indispensable and the measurement matrix needed to be designed to meet the conditions of CS [8][9][10]. These will be complicated and time-consuming, and the reconstructed images will be better when the real distribution tends to be sparser.…”

Section: Introductionmentioning

confidence: 99%

Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography

Zhang¹

2018

JEEE

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The compressed sensing algorithm based on gradient projection for spare reconstruction (CS-GPSR) is applied to electrical capacitance tomography (ECT) image reconstruction in this paper. First, using the orthogonal basis of FFT transformation, the grey signals of original images can be sparse. Secondly, the observation matrix of ECT system was designed by rearranging the exciting-measuring order, and the capacitance measurements and corresponding sensitivity matrix can be obtained. Finally, the reconstructed images can be obtained using CS-GPSR algorithm. Simulation experiments were carried out and the results showed that the reconstructed images with higher quality can be obtained using the presented CS-GPSR algorithm, compared with conventional linear back projection (LBP) and Landweber iterative algorithms.

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Sparsity-inspired image reconstruction for electrical capacitance tomography

Cited by 29 publications

References 29 publications

Prior Image Induced Regularization Method for Electrical Capacitance Tomography

Prior Image Induced Regularization Method for Electrical Capacitance Tomography

Direct Measurements of Permittivity of Plasma-Assisted Combustion Using Electrical Capacitance Tomography

Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography

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