Overview on Image Reconstruction Algorithms for Electrical Capacitance Tomography

Electrical Capacitance Tomography is an imaging technology for visualizing industrial process based on the mechanism of capacitance sensitive process. After decades of development, the research results of ECT technology are also constantly improved and further. However the soft-field effect and under-determined problem limited the ECT imaging technology applied in related field. There has been extensive effort to study the image reconstruction theory and algorithms, in order to significantly improve the image resolution and speed to extend the application of ECT technique. This paper mainly gives an overview for the current research status of image reconstruction algorithm and compares the shortcoming and advantages of the iterative reconstruction algorithms, the non-iterative reconstruction algorithms and intelligent algorithms. In the end, Several 3D image reconstruction algorithms are introduced and the future research direction of image reconstruction algorithm are discussed.

[1]  A. Adler,et al.  A Primal Dual-Interior Point Framework for Using the L 1-Norm or the L 2-Norm on the Data and Regularization Terms of Inverse Problems , 2009 .

[2]  Jzau-Sheng Lin,et al.  A Novel Image Reconstruction Algorithm for Electrical Capacitance Tomography , 2014, 2014 International Symposium on Computer, Consumer and Control.

[3]  Wuqiang Yang,et al.  An image-reconstruction algorithm based on Landweber's iteration method for electrical-capacitance tomography , 1999 .

[4]  Chi-Chung Cheung,et al.  Magnified gradient function with deterministic weight modification in adaptive learning , 2004, IEEE Transactions on Neural Networks.

[5]  Wuqiang Yang,et al.  Real-time 3D visualisation in electrical capacitance tomography , 2012, 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings.

[6]  Zhaoyan Fan,et al.  An adaptive total variation regularization method for electrical capacitance tomography , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[7]  Phaneendra K. Yalavarthy,et al.  Helmholtz-Type Regularization Method for Permittivity Reconstruction Using Experimental Phantom Data of Electrical Capacitance Tomography , 2010, IEEE Transactions on Instrumentation and Measurement.

[8]  Mona Noori Hosseini,et al.  Some refinements of the standard autoassociative neural network , 2012, Neural Computing and Applications.

[9]  Wang Xiaoxin,et al.  A self-adapting Landweber algorithm for the inverse problem of electrical capacitance tomography (ECT) , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[10]  Wuqiang Yang,et al.  Design of electrical capacitance tomography sensors , 2010 .

[11]  Haigang Wang,et al.  Image Recovery for Electrical Capacitance Tomography Based on Low-Rank Decomposition , 2017, IEEE Transactions on Instrumentation and Measurement.

[12]  Brian S. Hoyle,et al.  Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors , 1992 .

[13]  Fang Tianchi.  Comparison of Algebraic Reconstruction Technique and Simultaneous Iterative Reconstruction Technique in Electrical Capacitance Tomography Image Reconstruction , 2011 .

[14]  Hua Yan,et al.  Three-dimensional electrical capacitance tomography reconstruction by the Landweber iterative algorithm with fuzzy thresholding , 2014 .

[15]  Hua Yan,et al.  Comparisons of three modelling methods for the forward problem in three-dimensional electrical capacitance tomography , 2015 .

[16]  Krzysztof Grudzień,et al.  Application of ECT to solid concentration measurements during granular flow in a rectangular model silo , 2010 .

[17]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[18]  Cuihuan Li,et al.  A Fast Image Reconstruction Algorithm of Electrical Capacitance Tomography Based on Engle Criterion , 2012, 2012 Sixth International Conference on Electromagnetic Field Problems and Applications.

[19]  H. Yan,et al.  Image reconstruction in electrical capacitance tomography using multiple linear regression and regularization , 2001 .

[20]  Lihui Peng,et al.  Image reconstruction algorithms for electrical capacitance tomography , 2003 .

[21]  Jiamin Ye,et al.  A sparsity reconstruction algorithm for electrical capacitance tomography based on modified Landweber iteration , 2014 .

[22]  Roland Martin,et al.  Inversion of electrical capacitance tomography data by simulated annealing: Application to real two-phase gas-oil flow imaging , 2005 .

[23]  Sun Li-quan.  Simulation of Sensors and Image Reconstruction Algorithm Based on Genetic Algorithms for Electrical Capacitance Tomography System , 2004 .

[24]  Mao-Jiun J. Wang,et al.  Image thresholding by minimizing the measures of fuzzines , 1995, Pattern Recognit..

[25]  Po Box,et al.  Image reconstruction algorithms for electrical capacitance tomography: state of the art , 2004 .

[26]  M. Hanke.  Conjugate gradient type methods for ill-posed problems , 1995 .

[27]  Kwang Youn Kim,et al.  Modified iterative Landweber method in electrical capacitance tomography , 2006 .

[28]  Xi Chen,et al.  A Spatial Clustering Method With Edge Weighting for Image Segmentation , 2013, IEEE Geoscience and Remote Sensing Letters.

[29]  Andy Adler,et al.  A primal–dual interior-point framework for using the L1 or L2 norm on the data and regularization terms of inverse problems , 2012 .

[30]  Jianwei Li,et al.  An Image Reconstruction Algorithm Based on RBF Neural Network for Electrical Capacitance Tomography , 2012, 2012 Sixth International Conference on Electromagnetic Field Problems and Applications.