A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system
Image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system....
Saved in:
Main Authors: | Lee, Chau Ching, Rahiman, Mohd. Hafiz Fazalul, Leow, Pei Ling, Abdul Rahim, Ruzairi, Ahmad Saad, Fathinul Syahir |
---|---|
格式: | Article |
出版: |
Elsevier Ltd
2022
|
主題: | |
在線閱讀: | http://eprints.utm.my/id/eprint/99457/ http://dx.doi.org/10.1016/j.flowmeasinst.2022.102234 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
相似書籍
-
A supervised deep feedforward neural network (SDFNN)-based image reconstruction algorithm for radio tomographic imaging
由: Lee, Chau Ching, et al.
出版: (2021) -
Image reconstruction algorithms for ultrasonic tomography
由: Rahiman, Mohd. Hafiz Fazalul, et al.
出版: (2011) -
Eminent pixel reconstruction algorithm for ultrasonic tomography
由: Nor Ayob, Nor Muzakkir, et al.
出版: (2011) -
Image reconstruction using iterative transpose algorithm for optical tomography
由: Md. Yunos, Yusri, et al.
出版: (2007) -
A novel hybrid binary reconstruction algorithm for ultrasonic tomography
由: Abdul Rahim, R., et al.
出版: (2008)