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- W2980895991 abstract "It is known that EIT inversion is an ill-posed problem, meaning that the solution is unstable if noise exists in the measured data. Generally, a regularization scheme is needed to alleviate the ill-posedness. In this work, a multiplicative regularization scheme is applied to EIT inversion. In this regularization scheme, a cost functional is constructed in which the data misfit functional is multiplied by a regularization factor, and no regularization parameter is needed. The regularization factor is based on the weighted begin{document}$ L2 $end{document} -norm favoring 'blocky' profiles in the reconstructed images. Gauss–Newton method is used to minimize the cost functional iteratively. In the implementation of the multiplicative regularization scheme, the spatial gradient and divergence need to be computed on triangular meshes. For this purpose, the discrete exterior calculus (DEC) theory is applied to formulate the related discrete operators. Numerical and experimental results show good anti-noise performance of the multiplicative regularization scheme in EIT inverse problem." @default.
- W2980895991 created "2019-10-25" @default.
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- W2980895991 date "2019-01-01" @default.
- W2980895991 modified "2023-10-12" @default.
- W2980895991 title "Electrical impedance tomography with multiplicative regularization" @default.
- W2980895991 doi "https://doi.org/10.3934/ipi.2019051" @default.
- W2980895991 hasPublicationYear "2019" @default.
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