A new image reconstruction strategy for TMR-EMT: combining regularization theory with guided image filtering method

Abstract

Abstract Electromagnetic tomography based on tunneling magnetoresistance (TMR-EMT) can be used to obtain the solid phase (magnetic catalyst) distribution in a gas–liquid–solid three-phase fluidized bed based on changes in permeability. However, the TMR-EMT system has a higher sensitivity near the TMR sensor and lower sensitivity in other positions, which makes the ill-conditioned property of image reconstruction more serious compared with the traditional coil measurement EMT system. As a result, the reconstructed image quality of the solid phase distribution is low. Aiming to address this problem, a new image reconstruction strategy, based on guided image filtering (GIF) and regularization theory, is proposed for TMR-EMT to improve the reconstruction quality of the solid phase distribution in a gas–liquid–solid three-phase fluidized bed. First, the L2 regularization method and L1 regularization method are used to reconstruct the image of the permeability distribution in the region of interest (ROI). On this basis, the reconstructed images of the L2 regularization and L1 regularization are used as the input image and guided image of GIF respectively for filtering the output. Finally, the image of solid phase distribution in the ROI is obtained according to the reconstructed image of the permeability. Simulation and experimental results indicate that the proposed strategy can take into account the sparsity of L1 regularization and the smoothness of L2 regularization simultaneously, and obtain a higher image reconstruction quality.