.png){kind=icon}
Return
Deep Learning-Based Inversion Method for Imaging Problems in Electrical Capacitance Tomography
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2018.2811228.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Electrical capacitance tomography exhibits great potentials in the visualization measurement of industrial processes, and high-precision images are of great significance for the reliability and usefulness of measurement results. In this paper, we propose a deep learning-based inversion method to ameliorate the reconstruction accuracy. With the aid of the deep learning methodology, the prior from the images reconstructed by a certain imaging technique to the true images is abstracted and stored in the deep extreme learning machine. A new cost function is constructed to encapsulate the prior from the proposed deep learning model and the domain expertise about imaging targets, and the split Bregman algorithm and the fast iterative shrinkage thresholding technique are combined into a new numerical method to effectively solve it to get the final reconstruction. The numerical and experimental results validate that the inversion method proposed in this paper reduces the reconstruction artifacts and deformations and leads to the much improvement in the imaging quality.
Keywords:
Deep extreme learning machine (DELM)
deep learning
electrical capacitance tomography (ECT)
image reconstruction
inverse problem
reconstruction method
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
2.0W
Citations:
5.8W
Organization
View more organizations
Cited Papers
.png){kind=icon}
Stakeholder collaboration to support accountability in village fund management and rural development
OAAI
OAAIShareSaveShareSaveA Fluorometric Assay for DNA Cleavage Reactions Characterized with BamHI Restriction Endonuclease
OAAI
OAAIShareSaveShareSave View more