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Flat-Field Correction of X-Ray Tomographic Images Using Deep Convolutional Neural Networks

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Bulletin of the Russian Academy of Sciences: Physics Aims and scope

Abstract

It is proposed that neural networks be used to solve the problem of flat-field correction. A process is described for selecting parameters of a deep convolutional neural network in order to solve the problem of flat-field correction with the instability of an empty beam, training this network, and checking its operability on the generated data. The procedure is tested on data obtained with laboratory X-ray and synchrotron sources.

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Funding

The study was supported by the RF Ministry of Science and Higher Education within the framework of the state task of the Federal Research Center “Crystallography and Photonics” of the Russian Academy of Sciences in terms of the interpretation of tomographic data. The work on carrying out tomographic measurements was carried out with the support of the RF Ministry of Science and Higher Education as part of the work under grant no. 075-15-2021-1362.

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Authors and Affiliations

  1. Shubnikov Institute of Crystallography, Federal Research Center “Crystallography and Photonics,” Russian Academy of Sciences, 119333, Moscow, Russia

    A. Yu. Grigorev & A. V. Buzmakov

Authors
  1. A. Yu. Grigorev
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  2. A. V. Buzmakov
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Correspondence to A. Yu. Grigorev.

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The authors declare that they have no conflicts of interest.

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Translated by N. Petrov

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Grigorev, A.Y., Buzmakov, A.V. Flat-Field Correction of X-Ray Tomographic Images Using Deep Convolutional Neural Networks. Bull. Russ. Acad. Sci. Phys. 87, 604–610 (2023). https://doi.org/10.3103/S1062873822701684

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  • DOI: https://doi.org/10.3103/S1062873822701684

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