Electrical capacitance tomography aviation kerosene detection based on total variation and sparse regularization

Abstract

Aiming at the ill-posed problem of the solution in the image reconstruction process caused by the soft field effect of Electrical Capacitance Tomography (ECT) technology in the process of aviation kerosene detection, a hybrid minimization constraint model based on total variation joint sparse regularization is proposed. Applied to ECT image reconstruction, it can not only alleviate the artifact gradient in the smooth area of the reconstructed image, but also accurately approximate the optimal solution with appropriate sparse coefficients, and maintain the sharp edge of the reconstructed image. Since the multi-regularization brings more parameters, the Alternating Direction Method of Multipliers (ADMM) is selected to iteratively seek the optimal solution of the ECT inverse problem. The experimental results show that: compared with traditional algorithms such as Landweber and Tikhonov, the image reconstruction error using the algorithm in this paper has been significantly reduced, and the correlation coefficient has also been improved (in terms of the reconstruction effect of the core stream, the image reconstruction error is reduced to 0.03, The correlation coefficient is increased to 0.96), and the reconstructed image quality of different flow patterns is improved to a certain extent. The experimental results show that the ADMM algorithm based on the total variation joint sparse regularization mixed minimization model can effectively and accurately detect solid impurities in aviation kerosene.