Direct methods for image reconstruction in electrical capacitance tomography

Cao, Zhang; Xu, Lijun

doi:10.1016/b978-1-78242-118-4.00014-9

Cited by 3 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because reconstructed images in ECT generally have sparse features, the L 1 norm has the advantage of generating sparse solutions compared to other norms. Therefore, we deploy the L 1 -norm as the data fidelity term to enhance the accuracy of numerical solution [20], whose mathematical expression is as follows:…”

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Zhao,

Liu,

Chen

2024

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Zhao,

Liu,

Chen

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Nevertheless, it is generally considered not robust enough for industrial applications, as the reconstructed tomograms start to become erroneous when the monitored process has a smoothly changed electrical distribution. There are also other direct reconstruction methods such as Calderon's method and D-bar methods [70], which have also received lots of attentions recently.…”

Section: Reconstruction Algorithmsmentioning

confidence: 99%

Super-sensing technology: industrial applications and future challenges of electrical tomography

Wei

Qiu

Primrose

2016

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

One contribution of 10 to a theme issue 'Super-sensing through industrial process tomography' . Electrical tomography is a relatively new imaging technique that can image the distribution of the passive electrical properties of an object. Since electrical tomography technology was proposed in the 1980s, the technique has evolved rapidly because of its low cost, easy scale-up and non-invasive features. The technique itself can be sensitive to all passive electrical properties, such as conductivity, permittivity and permeability. Hence, it has a huge potential to be applied in many applications. Owing to its illposed nature and low image resolution, electrical tomography attracts more attention in industrial fields than biomedical fields. In the past decades, there have been many research developments and industrial implementations of electrical tomography; nevertheless, the awareness of this technology in industrial sectors is still one of the biggest limitations for technology implementation. In this paper, the authors have summarized several representative applications that use electrical tomography. Some of the current tomography research activities will also be discussed.This article is part of the themed issue 'Supersensing through industrial process tomography'.

show abstract

“…Improving the quality of the reconstructed images from these systems has attracted the attention of researchers. Some reconstructions algorithms have been proposed to address the problem [16][17][18][19][20][21][22][23][24]. Others have improved images from LBP by further preprocessing the images using different approaches such as filtering [25], segmentation [26] and curve fitting [27].…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Evaluation of Entropic Thresholding Algorithms Based on Shannon, Renyi and Tsallis Entropy Definitions for Electrical Capacitance Tomography Measurement Systems

Mwambela¹

2018

IJISA

View full text Add to dashboard Cite

Abstract-The concept of entropy as a measure of information has been extensively applied in information theory and related fields. The complex nature of information has resulted in some proposed entropy definitions. In image processing, the entropy concept has been used in developing thresholding techniques based on maximum entropy principles for image segmentation, enhancement and object detection purposes. In this article, entropy definitions are analysed to establish their relationship and after that evaluate their performance in image thresholding. Static simulated data from Electrical Capacitance Tomography measurement system for annular and stratified flows in multiphase hydrocarbons production has been used. Performance evaluation results of thresholding algorithms using Renyi entropy has shown to improve the measurements, particularly for stratified flow regimes. The improvement is solely based on the entropy definition, and it has been observed the introduced controlling parameters do not affect its performance. Renyi entropic thresholding algorithm is relatively robust as it is independent of the controlling parameter q and the grey level resolution. Therefore, there is the potential possibility of using Renyi entropic thresholding to improve measurements in hydrocarbons flow measurement using Electrical Capacitance Tomography measurement system.

show abstract

Direct methods for image reconstruction in electrical capacitance tomography

Cited by 3 publications

References 45 publications

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Super-sensing technology: industrial applications and future challenges of electrical tomography

Comparative Performance Evaluation of Entropic Thresholding Algorithms Based on Shannon, Renyi and Tsallis Entropy Definitions for Electrical Capacitance Tomography Measurement Systems

Contact Info

Product

Resources

About