Abstract
This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net - both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be effective in improving the quality of reconstructed tomographic images.
Highlights
Electrical impedance tomography (EIT) and electrical capacitance tomography (ECT) are based on the processing of data generated by the electrode system [1]
Comparing the reconstructions generated by the encoders and Elastic Net, you can see that the image created using Elastic Net is sharper
The reason may be the way of training the Elastic Net unit, which during the machine learning process uses both the input vectors and output values of the consecutive measuring cases
Summary
Electrical impedance tomography (EIT) and electrical capacitance tomography (ECT) are based on the processing of data generated by the electrode system [1]. In the case of reconstruction of tomographic images concerning objects with large dimensions and relatively low conductance, the data from the electrodes are often noisy. Thanks to the rapid technological development including data processing techniques, access to advanced computational methods is becoming easier each year. Costs of access to computing power and storage media are falling This promotes the development of computational techniques that use parallel computing and the processing of large data sets. The burden of compromise in which researchers are forced to choose between the time of calculation and the quality of results disappears It creates new possibilities in the field of application of algorithms for data smoothing and denoising [4, 5].
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