A Single Material Beam Hardening Reduction Approach for Industrial X-ray CT Images Utilizing Convolutional Neural Network
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
A Single Material Beam Hardening Reduction Approach for Industrial X-ray CT Images Utilizing Convolutional Neural Network ; volume:29 ; number:3 ; year:2024
E-Journal of nondestructive testing ; 29, Heft 3 (2024)
- Creator
-
Ghafarzadeh, Mahdi
Kejani, Mohammad Tavakoli
Asadi, Amirreza
Karimi, Mehdi
- DOI
-
10.58286/29264
- URN
-
urn:nbn:de:101:1-2502041057524.969436707097
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Ghafarzadeh, Mahdi
- Kejani, Mohammad Tavakoli
- Asadi, Amirreza
- Karimi, Mehdi
Other Objects (12)
Data-driven beam hardening correction for cone beam computed tomography
Utilizing convolutional neural networks to classify monkeypox skin lesions
Segmentation for mammography classification utilizing deep convolutional neural network
Validation of an easy-to-use beam-hardening measurement method
Refractive X-ray beam shaping
Case Study of Empirical Beam Hardening Correction Methods for Dimensional X-ray Computed Tomography Using a Dedicated Multi-material Reference Standard
Nondestructive Quantification of Heavy Elements Through the Analysis of Beam Hardening Artifacts Using Double-Exposure X-ray Computed Tomography: A Theoretical Consideration
Flame Hardening
Flexural performance of rapid-hardening concrete (RHC) beams with tension lap splice
Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
Utilizing convolutional neural networks to extract road features from Swiss historical maps
Monolithic focused reference beam x-ray holography
Data-driven beam hardening correction for cone beam computed tomography
Utilizing convolutional neural networks to classify monkeypox skin lesions
Segmentation for mammography classification utilizing deep convolutional neural network
Validation of an easy-to-use beam-hardening measurement method
Refractive X-ray beam shaping
Case Study of Empirical Beam Hardening Correction Methods for Dimensional X-ray Computed Tomography Using a Dedicated Multi-material Reference Standard
Nondestructive Quantification of Heavy Elements Through the Analysis of Beam Hardening Artifacts Using Double-Exposure X-ray Computed Tomography: A Theoretical Consideration
Flame Hardening
Flexural performance of rapid-hardening concrete (RHC) beams with tension lap splice
Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
Utilizing convolutional neural networks to extract road features from Swiss historical maps
Monolithic focused reference beam x-ray holography
Data-driven beam hardening correction for cone beam computed tomography
Utilizing convolutional neural networks to classify monkeypox skin lesions
Segmentation for mammography classification utilizing deep convolutional neural network
Validation of an easy-to-use beam-hardening measurement method
Refractive X-ray beam shaping
Case Study of Empirical Beam Hardening Correction Methods for Dimensional X-ray Computed Tomography Using a Dedicated Multi-material Reference Standard
Nondestructive Quantification of Heavy Elements Through the Analysis of Beam Hardening Artifacts Using Double-Exposure X-ray Computed Tomography: A Theoretical Consideration
Flame Hardening
Flexural performance of rapid-hardening concrete (RHC) beams with tension lap splice
Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
Utilizing convolutional neural networks to extract road features from Swiss historical maps