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Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography

Authors :
Shuangzhan Yang
Yu Han
Lei Li
Xiaoqi Xi
Siyu Tan
Linlin Zhu
Mengnan Liu
Bin Yan
Source :
Applied Sciences, Vol 12, Iss 22, p 11675 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

The mismatch of geometric parameters in a nanotomography system bears a significant impact on the reconstructed images. Moreover, projection image noise is increased due to limitations of the X-ray power source. The accuracy of the existing self-calibration method, which uses only the grayscale information of the projected image, is easily affected by noise and leads to reduced accuracy. This paper proposes a geometric parameter self-calibration method based on feature matching of mirror projection images. Firstly, the fast extraction and matching feature points in the mirror projection image are performed by speeded-up robust features (SURF). The feature triangle is then designed according to the stable position of the system’s rotation axis to further filter the feature points. In turn, the influence of the mismatched points on the calculation accuracy is reduced. Finally, the straight line where the rotation axis is located is fitted by the midpoint coordinates of the filtered feature points, thereby realizing geometric parameter calibration of the system. Simulation and actual data from the experimental results show that the proposed method effectively realizes the calibration of geometric parameters, and the blurring and ghosting caused by geometric artifacts are corrected. Compared with existing methods, the image clarity can be improved by up to 14.4%.

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.b9a07909036d4f978ba3e58cbb19fe94
Document Type :
article
Full Text :
https://doi.org/10.3390/app122211675