Image Filtering With Associative Markov Networks for ECT With Distinctive Phase Origins
Image Filtering With Associative Markov Networks for ECT With Distinctive Phase Origins
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DOI：
10.1109/jsen.2012.2192261
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发表时间：
2012-04
影响因子：
4.3
通讯作者：
Jiamin Ye
中科院分区：
文献类型：
--
作者：
Jiamin Ye
两相流电容层析成像（ECT）重建的图像在相界面处通常是模糊的。为了提高图像质量，图像滤波与关联马尔可夫网络（AMNs），它支持有效的图切割推理的绝缘分割，提出。研究了一种12电极电容层析成像传感器，并采用有限元法计算了几种典型介电常数分布下不同电极对之间的电容。初始图像分别采用线性反投影和Landweber迭代算法重建。然后使用AMN处理所获得的图像。仿真结果表明，图像的质量显着改善。
The images reconstructed by electrical capacitance tomography (ECT) for two-phase flows are usually blurry at the phase interface. To improve the image quality, image filtering with associative Markov networks (AMNs), which support efficient graph-cut inference for insulation segmentation, is presented. An ECT sensor with 12 electrodes is investigated and the capacitance between different electrode pairs is calculated for some typical permittivity distributions using a finite element method. The initial images are reconstructed by liner back-projection and Landweber iterative algorithm, respectively. The obtained images are then processed using AMNs. Simulation results show significant improvement in the quality of images.