在电容成像(E lectrica l C apac itance T om ography,ECT)中,为充分利用多次量测信息以提高电容成像图像重建质量,提出一种基于K a lm an滤波的电容成像图像重建算法。该算法重点考虑了测量噪声的影响,利用对流型一系列多次测量中获得的新息不断进行最优加权以获得重建图像的最小方差估计。针对3种典型介电常数分布进行了仿真,结果表明K a lm an滤波应用于ECT图像重建的可行性和有效性。提出了提高该算法运算速度的方案,分析和仿真结果表明通过预先计算最优滤波增益,并寻找合适的迭代次数,算法可快速地获得满意的图像重建结果。
Electrical capacitance tomography (ECT) has been used for more than a decade for imaging dielectric processes. However, because of its ill-posedness and non-linearity, ECT image reconstruction has always been a challenge. A new genetic algorithm (GA) developed for ECT image reconstruction uses initial results from a linear back-projection, which is widely used for ECT image reconstruction to optimize the threshold and the maximum and minimum gray values for the image. The procedure avoids optimizing the gray values pixel by pixel and significantly reduces the search space dimension. Both simulations and static experimental results show that the method is efficient and capable of reconstructing high quality images. Evaluation criteria show that the GA-based method has smaller image error and greater correlation coefficients. In addition, the GA-based method converges quickly with a small number of iterations.
