Image reconstruction for electrical capacitance tomography by using soft-thresholding iterative method with adaptive regulation parameter

Dong, Xiaofeng; Ye, Zhuoyi; Soleimani, Masoud

doi:10.1088/0957-0233/24/8/085402

Cited by 33 publications

(7 citation statements)

References 18 publications

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“…Different

can be obtained from different

. The optimal

can be determined by experiment, and l is set as 6 in this paper [ 27 ]. The program flowchart of adaptive soft-thresholding extreme learning machine (AST-ELM) model is shown in Figure 4 .…”

Section: Ast-elm Image Reconstructed Methodsmentioning

confidence: 99%

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT

Wang

Jia

et al. 2017

Sensors

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Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object.

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“…Different

can be obtained from different

. The optimal

can be determined by experiment, and l is set as 6 in this paper [ 27 ]. The program flowchart of adaptive soft-thresholding extreme learning machine (AST-ELM) model is shown in Figure 4 .…”

Section: Ast-elm Image Reconstructed Methodsmentioning

confidence: 99%

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT

Wang

Jia

et al. 2017

Sensors

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show abstract

“…where C is an m × 1 dimensional normalized capacitance vector, m value depends on the number of electrodes, S is an m × n dimensional sensitivity matrix representing the sensitivity of the capacitances change to changes in permittivity, and G is an n × 1 dimensional vector standing for the normalized permittivity distribution, i.e. the grey level of pixels for visualization [28].…”

Section: General Image Reconstruction Modelmentioning

confidence: 99%

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

Qing

Shi

Chai

et al. 2021

Meas. Sci. Technol.

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As an advanced detection technology in the industrial field, electrical capacitance tomography (ECT) can better reconstruct the material distribution state in the measured area by selecting the appropriate algorithm. In order to improve the reconstruction quality, this paper devises a novel objective function to model the ECT image reconstruction problem, in which L 1 -norm is deployed as data fidelity with the focus on weakening the influence of capacitance outliers on the reconstruction quality, L P regularization reinforces the sparseness of the phantom objects, and L 1 regularization applies to model deviation variables to increase the robustness of the system. Based on the fast-iterative shrinkage thresholding technique and the soft thresholding method as sub-solvers, the split Bregman iterative method is designed as an effective solver for the proposed objective function. Numerical simulation and experimental validate that the proposed algorithm has excellent imaging ability compared with other imaging algorithms, which provides a good choice for the practical application of ECT in the future.

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“…To overcome the difficulties in the non-iterative reconstruction methods, researchers resort to the iterative imaging method. Frequently-used iterative reconstruction techniques include the Landweber algorithm [3]- [5], the algebraic reconstruction technique (ART) [6], the sparsity reconstruction technique [7]- [9], the split Bregman algorithm [10], the model-based imaging technique [11], the level set method [12], the fast linearized alternating direction method of multipliers [13], the simulated annealing particle swarm optimization method [14], the shape-energy evolutionary reconstruction algorithm [15], the total variation (TV) regularization method [16]- [18], the robust principle component analysis based imaging method [19], the low n-rank constraint reconstruction technique [20], the multicriterion optimization image reconstruction technique [21], etc. Unlike the L2R technique and the OIOR algorithm, iteration reconstruction techniques require multiple iterations before obtaining a reasonable and reliable result.…”

Section: Introductionmentioning

confidence: 99%

“…Regularization imaging methods, with a good flexibility in dealing with inverse problems from different application scenarios, are one of powerful strategies for solving the image reconstruction problem, such as the L1R method [7] and the TV method [16], [17], etc. There are two critical components in a regularization reconstruction method, i.e., the construction of cost function, including the model and extraction of the prior information, etc., and the design of computation method.…”

Section: Introductionmentioning

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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Image reconstruction for electrical capacitance tomography by using soft-thresholding iterative method with adaptive regulation parameter

Cited by 33 publications

References 18 publications

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT

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

Prior Image Induced Regularization Method for Electrical Capacitance Tomography

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