A image reconstruction algorithm based on variation regularization for magnetic induction tomography

This paper presents a variation regularization image reconstruction algorithm based on 1-norm which solves the ill-posed inverse problem of magnetic induction tomography (MIT) and improves the quality of reconstructed image. The variation regularization algorithm, compared with Tikhonov regularization algorithm based on 2-norm, overcomes the numerical instability of MIT image reconstruction and improves the resolving power of targets conductors and the quality of the reconstructed image, and it also makes the dividing line between target conductors region and background region clearer. Simulation results show that the quality of the reconstructed image obtained using the presented algorithm is enhanced, so an effective method for MIT is introduced.