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.
Methods:
In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image
reconstruction method with MIT system is presented.
Results:
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 illposed
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:
Experiments show that the proposed method has better indicators such as image error
and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high
accuracy.
Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network
