3-D Image Reconstruction in Planar Array ECT by Combining Depth Estimation and Sparse Representation

Suo, Peng; Sun, Jiangtao; Tian, Wenbin; Sun, Shijie; Xu, Lijun

doi:10.1109/tim.2021.3086903

Cited by 26 publications

(6 citation statements)

References 31 publications

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“…The L1 regularization method solves the inverse problem through the sparsity of the solution, which has good noise suppression and edge preserving characteristics. Equation ( 9) can be solved by the Gauss Newton method [27]. Since ( 9) is not differentiable, the approximate form of (9) needs to be obtained, by introducing a parameter β, which can be written as…”

Section: Regularization Methodsmentioning

confidence: 99%

A new image reconstruction strategy for TMR-EMT: combining regularization theory with guided image filtering method

Wang¹,

Guo²,

Li³

et al. 2022

Meas. Sci. Technol.

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Electromagnetic tomography based on tunneling magnetoresistance (TMR-EMT) can be used to obtain solid phase (magnetic catalyst) distribution in gas-liquid-solid three-phase fluidized bed based on the permeability change. However, the TMR-EMT system has higher sensitivity near the TMR sensor and lower sensitivity in other positions, which makes the ill-conditioned property of image reconstruction more serious compared with traditional coil measurement EMT system. As a result, the reconstructed image quality of the solid phase distribution is low. Aiming at the problem, a new image reconstruction strategy based on guided image filtering (GIF) and regularization theory is proposed for TMR-EMT to improve the reconstruction quality of solid phase distribution in gas-liquid-solid three-phase fluidized bed. First, L2 regularization method and L1 regularization method are used to reconstruct the image of the permeability distribution in the region of interest (ROI). On this basis, the reconstructed images of L2 regularization and L1 regularization are used as the input image and guided image of GIF respectively for filtering output. Finally, the image of solid phase distribution in the ROI is obtained according to the reconstructed image of permeability. Simulation and experimental results indicate that the proposed strategy can take into account the sparsity of L1 regularization and the smoothness of L2 regularization simultaneously, and obtain higher image reconstruction quality.

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Section: Regularization Methodsmentioning

confidence: 99%

A new image reconstruction strategy for TMR-EMT: combining regularization theory with guided image filtering method

Wang¹,

Guo²,

Li³

et al. 2022

Meas. Sci. Technol.

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show abstract

“…where P(V) is the data fidelity term, which measures the difference between HV and Y, Q i (V) is the regularizer, and λ i is the regularization parameter, which adjusts the proportion of the data fidelity term and the regularizer, and λ i > 0 [19].…”

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Zhao,

Liu,

Chen

2024

Applied Sciences

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High-precision images enable electrical capacitance tomography (ECT) to obtain more reliable measurement results, meaning that the reconstruction algorithm is particularly important. Some excellent numerical algorithms have successfully solved the inverse problem for ECT imaging, but their imaging quality is relatively low. To solve this problem, this paper proposes a new reconstruction algorithm based on regularized extreme learning machines (RELMs). The implementation of the algorithm is mainly divided into two steps: (1) according to a large number of training samples, the RELM model can be obtained by the iterative split Bregman (ISB) algorithm, which can describe the mapping relationship between the capacitance correlation coefficient and the imaging target well, and (2) the capacitance correlation coefficient is calculated, which is then used as input to the RELM model to predict the final imaging. Both simulation and experimental results show that the RELM algorithm achieves greater improvement in imaging quality and robustness, and provides new development ideas for the ECT.

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“…This imaging method is called indirect 3D imaging, also called quasi-3D imaging. In some views does not make this imaging a true 3D imaging, because he is only a rough approximation of imaging [13] .…”

Section: Direct 3d Imagingmentioning

confidence: 99%

Portable package detection imaging method based on planar ECT technology

Chen

Zhang

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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In order to realize the rapid detection and imaging of dangerous goods with high dielectric constant at small and mediumsized express points, a three-dimensional imaging method for packaging inspection based on planar ECT technology was proposed; Firstly, the electrode matrix of traditional planar ECT is optimized, and a planar ECT sensor with linear detection electrode array is designed; Then, the simulation model of the package to be tested under different dielectric constants is established. During the detection, the electrode plate is placed on the top of the package and moved slowly to obtain the capacitance signal of the detection electrode plate in different areas of the package; Finally, the iso-surface method is used for partial and full 3D imaging of the object in the package. The experimental results show that the designed detection method can realize three-dimensional imaging of high water content in packaging and metal dangerous goods.

show abstract

3-D Image Reconstruction in Planar Array ECT by Combining Depth Estimation and Sparse Representation

Cited by 26 publications

References 31 publications

A new image reconstruction strategy for TMR-EMT: combining regularization theory with guided image filtering method

A new image reconstruction strategy for TMR-EMT: combining regularization theory with guided image filtering method

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Portable package detection imaging method based on planar ECT technology

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