A New Hybrid Image Reconstruction Algorithm for Magnetic Induction Tomography

Abstract:

A new hybrid algorithm is presented in this paper, which solves the ill-posed inverse problem of magnetic induction tomography (MIT) and improves the quality of reconstructed image. The hybrid algorithm firstly produces the preliminary image region using Tikhonov regularization algorithm, and then it obtains the final reconstructed image using variation regularization algorithm. The hybrid algorithm, compared with the Tikhonov regularization algorithm and the variation regularization algorithm, overcomes the numerical instability of MIT image reconstruction and accelerates the convergence speed of image reconstruction, and it also improves the resolving power of targets conductor and the quality of the reconstructed image. Simulation results show that the quality of the reconstructed image obtained using the hybrid algorithm is enhanced, so an effective algorithm for magnetic induction tomography (MIT) is introduced.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1706-1710
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1706
• Online since: June 2012
• Authors: Yu Yan Chen, Xu Wang, Dan Yang, Yi Lv
• Keywords: Hybrid Algorithm, Magnetic Induction Impedance Tomography (MIT), Reconstructed Image, Tikhonov Regularization, Variation Regularization

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