Reconstruction method with the learned regularizer for imaging problems in electrical capacitance tomography

Lei, Jing; Liu, Qibin

doi:10.1016/j.asoc.2020.106126

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…Moreover, in practical industrial applications, it may be affected by measurement conditions, noise, and other factors. Therefore, even if a real vector of normalized dielectric constant is substituted, there will still be differences between the two sides of equation ( 14) [33]. The problem belongs to the fuzzy characteristics of the ECT technique, and the fuzzy programming algorithm is introduced in the image reconstruction work, and proposes the fuzzy nonlinear programming algorithm based on second-order hybrid sensitivity matrix:…”

Section: Fuzzy Nonlinear Programming Algorithm Based On Second-order ...mentioning

confidence: 99%

Image reconstruction of ECT based on second-order hybrid sensitivity matrix and fuzzy nonlinear programming

Zhang,

Chen

2023

Meas. Sci. Technol.

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In the derivation of the sensitivity matrix of electrical capacitance tomography, only the linear portion of the sensitivity coefficient is usually retained, while the neglected nonlinear part also contains important imaging information. In order to improve the accuracy of image reconstruction, a second-order hybrid sensitivity matrix is presented based on the capacitance normalization model and second-order item of sensitivity coefficients in this paper. Then, a fuzzy nonlinear programming algorithm based on the second-order hybrid sensitivity matrix (SHS-FNP) is proposed. Simulation and experiments are carried out. Reconstructed images using the presented method are compared with those of the Tikhonov algorithm, first-order hybrid Landweber algorithm, second-order hybrid Landweber algorithm, and fuzzy linear programming algorithm. The experimental results show that the SHS-FNP algorithm considerably enhances the quality of the reconstructed image.

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Section: Fuzzy Nonlinear Programming Algorithm Based On Second-order ...mentioning

confidence: 99%

Image reconstruction of ECT based on second-order hybrid sensitivity matrix and fuzzy nonlinear programming

Zhang,

Chen

2023

Meas. Sci. Technol.

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Coplanar capacitive sensors and their applications in non-destructive evaluation: a review

Mwelango

Zhu

Wen

et al. 2023

Nondestructive Testing and Evaluation

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A PI^λD^μ-Controlled Calderon’s Method for Triple-Valued Electrical Capacitance Tomography

Tian,

Cao,

et al. 2023

IEEE Trans. Instrum. Meas.

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Reconstruction method with the learned regularizer for imaging problems in electrical capacitance tomography

Cited by 3 publications

References 57 publications

Image reconstruction of ECT based on second-order hybrid sensitivity matrix and fuzzy nonlinear programming

Image reconstruction of ECT based on second-order hybrid sensitivity matrix and fuzzy nonlinear programming

Coplanar capacitive sensors and their applications in non-destructive evaluation: a review

A PI^λD^μ-Controlled Calderon’s Method for Triple-Valued Electrical Capacitance Tomography

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Reconstruction method with the learned regularizer for imaging problems in electrical capacitance tomography

Cited by 3 publications

References 57 publications

Image reconstruction of ECT based on second-order hybrid sensitivity matrix and fuzzy nonlinear programming

Image reconstruction of ECT based on second-order hybrid sensitivity matrix and fuzzy nonlinear programming

Coplanar capacitive sensors and their applications in non-destructive evaluation: a review

A PI λ D μ -Controlled Calderon’s Method for Triple-Valued Electrical Capacitance Tomography

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Product

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A PI^λD^μ-Controlled Calderon’s Method for Triple-Valued Electrical Capacitance Tomography