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提出一种基于蒂洪诺夫(Tikhonov)迭代的的电荷层析(electrostatic tomography,EST)成像算法。通过Tikhonov迭代法得到初始荷电粒子的图像分布,然后采用最大熵的方法设置门槛灰度对重建结果进行滤波提高图像的可分辨性。并针对Tikhonov迭代正则化系数难以选择的问题提出灵敏场奇异值分解的方法解决,选取灵敏场的最大奇异值作为正则化系数。仿真和实验结果表明:该算法具有收敛速度快,重建图像可分辨性高的优点。
A Tikhonov iterative electrostatic tomography (EST) imaging algorithm is proposed. The image distribution of the initial charged particles is obtained by Tikhonov iteration method. Then, the maximum entropy method is used to set the threshold grayscale to filter the reconstruction results to improve the image resolution. In order to solve the problem that Tikhonov iterative regularization coefficient is difficult to choose, the method of solving the sensitive field singular value decomposition is proposed. The maximum singular value of the sensitive field is selected as the regularization coefficient. Simulation and experimental results show that the proposed algorithm has the advantages of fast convergence and high resolution of reconstructed images.