A hybrid of iterative Gauss–Newton and one-dimensional convolutional neural network for high-resolution electrical impedance tomography

A hybrid of iterative Gauss–Newton and one-dimensional convolutional neural network for high-resolution electrical impedance tomography

We developed a processing method using benefits of both iterative Gauss–Newton (IGN) and a one-dimensional convolutional neural network (1D-CNN) for high-resolution electrical impedance tomography. The proposed method logically combines conductivity images reconstructed by different methods. The acc...

Full description

Bibliographic Details
Main Authors: Keiya Minakawa, Keigo Ohta, Hiroaki Komatsu, Tomoko Fukuyama, Takashi Ikuno
Format: Article
Language:English
Published: AIP Publishing LLC 2024-01-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/5.0185371

Internet

http://dx.doi.org/10.1063/5.0185371

Similar Items