Information system for neural network analysis of x-ray Moiré images using wavelet filtering

Ihor M. Fodchuk; Mariana Borcha; Ivanna Yanchuk

doi:10.1117/12.3016096

5 January 2024 Information system for neural network analysis of x-ray Moiré images using wavelet filtering

Ihor M. Fodchuk, Mariana Borcha, Ivanna Yanchuk

Proceedings Volume 12938, Sixteenth International Conference on Correlation Optics; 129382K (2024) https://doi.org/10.1117/12.3016096
Event: International Conference Correlation Optics (COR 2023), 2023, Chernivtsi, Ukraine

Abstract

An information system has been developed for the analysis of x-ray moiré images using artificial neural networks and wavelet filtering. Moiré images are obtained as a result of the action of a set of forces on the surface of the crystal. The analysis of the images consisted in solving the inverse problem, that is, in calculating the values of a set of forces based on the moiré image. To prevent retraining of the neural network, a control dataset of images was used. The input signals of the artificial neural network are moiré images after contour extraction, logarithmization, downscaling and wavelet filtering using Dobeshe wavelets. Due to such image processing, the training time of the neural network was reduced with a sufficiently low training error. The output signals of the neural network are the values of a set of forces that formed the moiré image. The training of an artificial neural network is performed by the method of backpropagation. The artificial neural network is implemented in the Python language in the Google Colab cloud platform. The results of testing the developed information system showed a high accuracy of calculating the values of a set of forces for simulated and experimental x-ray moiré images.

Citation Download Citation

Ihor M. Fodchuk, Mariana Borcha, and Ivanna Yanchuk "Information system for neural network analysis of x-ray Moiré images using wavelet filtering", Proc. SPIE 12938, Sixteenth International Conference on Correlation Optics, 129382K (5 January 2024); https://doi.org/10.1117/12.3016096

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KEYWORDS

Artificial neural networks

Wavelets

Crystals

X-ray imaging

X-rays

Tunable filters

Image filtering

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