Adaptive Image Reconstruction Algorithm for Electrical Capacitance Tomography

Since the image reconstruction of electrical capacitance tomography (ECT) is an inverse problem of ill-posedness, regularization techniques are widely used to obtain stable solutions. The choice of regularization coefficients has a direct influence on the reconstruction result. In theory, the optimal regularization coefficient exists, but for most regularization techniques, the computation cost is too large to apply in real-time ECT image reconstruction. In this paper, a new approach which can determine the regularization coefficient quickly in iterative Tikhonov regularization algorithm for ECT is proposed. In order to improve the quality of the reconstructed image further, an improved method of adaptive threshold filtering is used in the last Tikhonov iteration. The algorithm proposed is tested by the noise-free and noise-contaminated capacitance data. The simulation results demonstrate that the proposed algorithm is superior to well-known Landweber algorithm in terms of image quality.