Jingwen Wang*, Xu Wang, Dan Yang and Kaiyang Wang Pages 1 - 8 ( 8 )
Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges.
Discussion: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the ill-posed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation.
Conclusion: Experimentals show that the proposed method with better indicators such as image error and correlation coefficient. So it is a kind of MIT image reconstruction method with high accuracy.
Magnetic induction tomography (MIT), imaging reconstruction, Fourier coefficients, Finite rate of innovation (FRI), sparsity
College of Information Engineering, China Jiliang University, Hangzhou 310018, School of Information Science & Engineering, Northeastern University, Shenyang 110819, School of Information Science & Engineering, Northeastern University, Shenyang 110819, School of Information Science & Engineering, Northeastern University, Shenyang 110819