Prior Image Induced Regularization Method for Electrical Capacitance Tomography
The image reconstruction is a crucial step in the electrical capacitance tomography. This paper presents a new methodology for improving the reconstruction accuracy. The prior image induced regularization from the deep convolutional extreme learning machine (DCELM) is introduced, which is integrated...
Main Authors: | Pan Chu, Jing Lei, Qibin Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8572696/ |
Similar Items
-
Comparison of no-prior and soft-prior regularization in biomedical microwave imaging
by: Amir H Golnabi, et al.
Published: (2011-01-01) -
An Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Robust Principle Component Analysis
by: Xueyao Wang, et al.
Published: (2013-02-01) -
EVALUATION OF THE ELECTRICAL CAPACITANCE TOMOGRAPHY SYSTEM FOR MEASUREMENT USING 3D SENSOR
by: Jacek Kryszyn, et al.
Published: (2019-12-01) -
Dynamic PET Image Denoising Using Deep Image Prior Combined With Regularization by Denoising
by: Hao Sun, et al.
Published: (2021-01-01) -
Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography
by: Zhuoran Chen, et al.
Published: (2021-04-01)