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Image Reconstruction Based on Fractional Tikhonov Framework for planar array capacitance sensor

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2022年 8卷 109-120页

作者： Sun, Yurong Zhang, Yuyan Wen, Yintang Yanshan Univ Key Lab Measurement Technol & Instrumentat Hebei Qinhuangdao 066004 Hebei Peoples R China

planar array capacitance sensor imaging is a nonde-structive imaging technique that can provide the permittivity distribution of media across capacitance data measured by electrodes. However, the image reconstruction problem is ill-posed because the dimensions of sensitive field data and the capacitance data are substantially unmatched, which leads to unstable imaging and low imaging accuracy. This paper proposes an imaging methodology of the fractional Tikhonov framework with automatic parameter selection to visualize the permittivity distribution of the object field. The proposed methodology replaces the severely ill-posed inverse problem with a penalty least squares problem. The fractional power of the Moore-Penrose pseudoinverse is utilized as the weighting matrix to measure the residual error in regularization. A synchronous iterative automatic selection method is proposed instead of empirical selection to realize the automatic determination of fractional power and fractional regularization parameters. In addition, the residual error in the regularization process is reduced and the quality of the solution is improved. The numerical experiments indicated that the proposed algorithm can ensure the accuracy of the solution and can achieve high precision reconstruction images.

关键词： Defect detection image reconstruction fractional framework automatic parameter selection planar array capacitance sensor

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An imaging algorithm of planar array capacitance sensor for defect detection

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MEASUREMENT 2021年 168卷 108466-108466页

作者： Zhang, Yuyan Sun, Yurong Wen, Yintang Yanshan Univ Key Lab Measurement Technol & Instrumentat Hebei Qinhuangdao 066004 Hebei Peoples R China

planar array capacitance imaging is a feasible solution for detecting defects in composite components. However, the coplanar arrangement of electrodes in the imaging system renders a soft field effect, which results in unstable or susceptible imaging. The inverse problem of a planar array capacitance sensor system is ill-posed. Hence, a wavelet fusion combined multi-objective threshold programming imaging algorithm is proposed for a planar array capacitive imaging system. Furthermore, initial values of conjugate gradient (CG) and Newton-Raphson (NR) algorithms are optimized using the Tikhonov regularization algorithm. Subsequently, wavelet fusion is introduced to fuse images obtained using the CG and NR algorithms to acquire more detailed information. To further improve the reconstructed quality, a multi-objective threshold programming strategy is proposed. The final reconstruction image is obtained using the optimal threshold. Experimental results show a significantly improved quality of the reconstructed image, thereby verifying the effectiveness of the presented algorithm.

关键词： planar array capacitance sensor Image reconstruction algorithm Wavelet fusion Multi-objective threshold programming

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An optimization method of planar array capacitance imaging

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sensorS AND ACTUATORS A-PHYSICAL 2021年 327卷 112724-112724页

作者： Pan, Zhao Wang, Shan Li, Pengcheng Zhang, Yuyan Wen, Yintang Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Sch Sci & Technol Qinhuangdao 066004 Hebei Peoples R China

planar array capacitance imaging technology is a kind of nondestructive testing technology applied to defect detection of composite materials. In the imaging process, the image quality is often poor due to the environment noise and the ill-posed of inverse problem solving. In this paper, an image reconstruction optimization method is proposed. Based on the analysis of the sensitive region of a single pair of electrodes and the contribution of capacitance value to each solution unit, an optimization matrix which is calculated by adjusting the contribution coefficient of each capacitance value to different solution units is proposed to optimize the sensitive field. Finally, simulation and experiment results are presented to show the effectiveness of the proposed method. Through the average relative error of each pixel of the reconstructed image before and after optimization, it can be seen that the error of reconstructed images at two different positions are reduced by an average of 3.22 % and 1.62 % respectively, while verified this method can effectively improve the reconstructed image and improve the anti-noise ability of the image. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Anti-noise optimization matrix Image reconstruction Nondestructive testing planar array capacitance sensor

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