基于广义矢量模式匹配的静电层析成像新方法

A novel electrostatic tomograpgy algorithm based on generalized vector sample pattern matching

在分析静电层析成像原理及其正、逆问题的基础上,针对静电层析成像系统传感器获得的独立电荷测量数据少的问题,提出了基于广义矢量模式匹配的图像重建算法。该算法作为一种迭代算法,是基于输入向量与解向量之间的夹角为最小的目标准则,在迭代过程中无需设置任何经验值就能稳定地重建图像。Mathematica仿真结果表明,广义矢量模式匹配算法与线性反投影(LBP)算法和迭代Tikhonov正则化(ITR)算法相比,在图像误差和图像相关量参数方面均较为优越。

Aiming at an electrostatic tomography （EST） system＇ s shortage of acquired measurement data for independent tern matching （GVSPM） based on the analysis of the EST principle and its forwards and inverse problem. As an iterative method, the GVSPM is based on the standard for the minimum angle between the input vector and the solution vector, and it can converge upon a reconstructed image stably without setting an empirical gain value in the iterative process. The Mathematica simulation results showed that this method was superior in image error and image correlation to the linear back projection （LBP） method and the iterative Tikhonov regularization （ITR） method.