Image reconstruction based on frequency domain feature extraction for EMT

Abstract

Image reconstruction of electromagnetic tomography (EMT) is often ill-posed due to the limited prior information about imaging features, leading to low reconstruction accuracy. In this paper, a novel image reconstruction method based on frequency domain feature extraction for EMT is proposed to improve the reconstruction accuracy. The two-dimensional EMT image is first modeled as a one-dimensional finite-length streams of Dirac signal, which is a typical signal with finite rate of innovation (FRI). Then the reconstruction results of the recent algorithms, such as the LBP, Landweber and TV algorithms, can be also modeled as FRI signals. The frequency domain feature of such signals can be extracted with a distributed FRI sampling system. Based on the obtained FRI samples, a new EMT measurement equation is established, and a joint image reconstruction algorithm is proposed, to improve the quality of the reconstructed image. Finally, simulation results have shown that the proposed method outperforms the related methods with better indicators such as image error and correlation coefficient.

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

“…The corresponding model of physical field can be established due to the fact that EMT satisfies the Maxwell equations as shown in Eq. (1) [17,18].…”

Design and optimization of electromagnetic tomography and electrical resistance tomography dual-modality sensor

“…Electrical tomography (ET) is an important method for detecting the phase distribution in the fluidized bed [3][4][5][6]. * Authors to whom any correspondence should be addressed.…”