Image reconstruction using a genetic algorithm for electrical capacitance tomography

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.

[1]  Wuqiang Yang,et al.  Hardware design of electrical capacitance tomography systems , 1996 .

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

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

[5]  G Teague,et al.  Neural network reconstruction for tomography of a gravel-air-seawater mixture , 2001 .

[6]  Ø. Isaksen,et al.  A review of reconstruction techniques for capacitance tomography , 1996 .

[7]  Dieter Mewes,et al.  Investigation of the two-phase flow in trickle-bed reactors using capacitance tomography , 1997 .

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

[9]  Richard A Williams,et al.  Process tomography: a European innovation and its applications , 1996 .

[10]  G. E. Fasching,et al.  A capacitive system for three‐dimensional imaging of fluidized beds , 1991 .

[11]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[12]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[13]  Wuqiang Yang,et al.  Electrical Capacitance Tomography — from Design to Applications , 1995 .

[14]  Maurice Beck,et al.  Tomographic imaging of two-component flow using capacitance sensors , 1989 .

[15]  Maurice Beck,et al.  Experimental evaluation of capacitance tomographic flow imaging systems using physical models , 1994 .

[16]  M. S. Beck,et al.  Capacitance-based tomographic flow imaging system , 1988 .

[17]  Dieter Mewes,et al.  Recent developments and industrial/research applications of capacitance tomography , 1996 .

[18]  Tomasz Dyakowski,et al.  Application of capacitance tomography to gas-solid flows , 1997 .

[19]  Tomasz Dyakowski,et al.  Process tomography - the state of the art , 1998 .