Flat-field correction on X-ray tomographic images using deep convolutional neural networks

А. Yu. Grigorev; Григорьев А. Ю.; А. V. Buzmakov; Бузмаков А. В.

doi:10.31857/S0367676522701149

Flat-field correction on X-ray tomographic images using deep convolutional neural networks

作者: Grigorev А.Y.¹, Buzmakov А.V.¹
隶属关系:
1. Shubnikov Institute of Crystallography of the Federal Scientific Research Centre “Crystallography and Photonics” of the Russian Academy of Sciences
期: 卷 87, 编号 5 (2023)
页面: 685-691
栏目: Articles
URL: https://rjsvd.com/0367-6765/article/view/654406
DOI: https://doi.org/10.31857/S0367676522701149
EDN: https://elibrary.ru/KVWGZD
ID: 654406

如何引用文章

全文:

开放存取

##reader.subscriptionAccessGranted##
受限制的访问

订阅存取

详细
作者简介
参考
补充文件
统计

详细

We proposed to use neural networks to solve the problem of flat-field correction. We described the process of selecting parameters of a deep convolutional neural network to solve the flat-field correction problem with the instability of an empty beam, describes the training of this network, and checks its operability on the generated data. The developed method was tested on data obtained both on laboratory X-ray sources and synchrotron sources.

作者简介

А. Grigorev

Shubnikov Institute of Crystallography of the Federal Scientific Research Centre “Crystallography and Photonics”
of the Russian Academy of Sciences

编辑信件的主要联系方式.
Email: grigorev.a@crys.ras.ru
Russia, 119333, Moscow

А. Buzmakov

Shubnikov Institute of Crystallography of the Federal Scientific Research Centre “Crystallography and Photonics”
of the Russian Academy of Sciences

Email: grigorev.a@crys.ras.ru
Russia, 119333, Moscow

参考

Landis E.N., Keane D.T. // Mater. Charact. 2010. V. 61. No. 12. P. 1305.
Seibert J.A., Boone J.M., Lindfors K.K. // Proc. SPIE. 1998. V. 3336. P. 348.
Nieuwenhove V.V., Beenhouwer J.D., Carlo F.D. et al. // Opt. Express. 2015. V. 23. No. 21. P. 27975.
Hagemann J., Vassholz M., Hoeppe H. et al. // J. Synchrotron Radiat. 2021. V. 28. No. 1. P. 52.
Buakor K., Zhang Yu., Birnšteinová Š et al. // Optics Express. 2022. V. 30. No. 7. P. 10633.
LeCun Y., Bengio Y., Hinton G. // Nature. 2015. V. 521. No. 7553. P. 436.
Van Dyk D.A., Meng X.L. // J. Comput. Graphical Stat. 2001. V. 10. No. 1. P. 1.
Бузмаков А.В., Асадчиков В.Е., Золотов Д.А. и др. // Кристаллография. 2018. Т. 63. № 6. С. 1007.
Tlustos L., Campbell M., Heijne E., Llopart X. // 2003 IEEE Nuclear Science Symposium. V. 3. (Portland, 2003) P. 1588.
Goodfellow I., Pouget-Abadie J., Mirza M. et al. // Advances in Neural Information Processing Systems. V. 27. (Montreal, 2014). P. 1.
Ronneberger O., Fischer P., Brox Th. // Internat. Conf. Medical Image Computing and Computer-assisted Intervention. (Munich, 2015). P. 1.
Ledig C., Theis L., Huszar F. et al. // Proc. IEEE Conf. Computer Vision and Pattern Recognition. (Honolulu, 2017). P. 4681.
Wang L.T., Hoover N.E., Porter E.H., Zasio J.J. // Proc. 24th ACM/IEEE Design Automation Conference. (Miami Beach, 1987). P. 2.
Ruder S. // arXiv: 1609.04747. 2016.