Abstract
Electrical capacitance tomography (ECT) has been used to measure flow by applying gas-solid flow in coal gasification, pharmaceutical, and other industries. ECT is also used for creating images of physically confined objects. The data collected by the acquisition system to produce images undergo blurring because of ambient conditions and the electronic circuitry used. This research includes the principle of ECT techniques for deblurring images that were created during measurement. The data recorded by the said acquisition system ascends a large number of linear equations. This system of equations is sparse and ill-conditioned and hence is ill-posed in nature. A variety of reconstruction algorithms with many pros and cons are available to deal with ill-posed problems. Large-scale systems of linear equations resulting during image deblurring problems are solved using iterative regularization algorithms. The conjugate gradient algorithm for least-squares problems (CGLS), least-squares QR factorization (LSQR), and the modified residual norm steepest descent (MRNSD) algorithm are the famous variations of iterative algorithms. These algorithms exhibit a semiconvergence behavior; that is, the computed solution quality first improves and then reduces as the error norm decreases and later increases with each iteration. In this work, soft thresholding has been used for image deblurring problems to tackle the semiconvergence issues. Numerical test problems were executed to indicate the efficacy of the suggested algorithms with criteria for optimal stopping iterations. Results show marginal improvement compared to the traditional iterative algorithms (CGLS, LSQR, and MRNSD) for resolving semiconvergence behavior and image restoration.
Highlights
Industrial tomography offers a prospect to measure and visualize the physically constrained industry operations [1]
electrical capacitance tomography (ECT) is commonly used in many other imaging modalities for visualization besides magnetic resonance imaging (MRI) [6] and X-ray tomography [7]
Our analysis shows that modified residual norm steepest descent (MRNSD) and soft thresholding are both convergent algorithms. e computational tests demonstrate that the suggested algorithms resolve semiconvergence and restoration behaviors. e results of conjugate gradient algorithm for least-squares problems (CGLS), least-squares QR factorization (LSQR), and MRNSD are only marginally better than their optimal iterations [29]
Summary
Industrial tomography offers a prospect to measure and visualize the physically constrained industry operations [1]. E latter group, named soft field, comprises microwave tomography (MWT), electrical impedance tomography (EIT), and electrical capacitance tomography (ECT) [2, 3]. ECT is applicable in rough ambient conditions where its tolerance to high temperature and pressure is required. It is less priced, fast, safe, and easy to use. ECT can offer extra data on the gas-solid flow hydrodynamics [5], for example, Mathematical Problems in Engineering flow transition from bubbling to slowing flux for scaling the CFB process from the laboratory scale to the production scale with diverse riser structures and cyclones. ECT is commonly used in many other imaging modalities for visualization besides magnetic resonance imaging (MRI) [6] and X-ray tomography [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
