A comparison of deep‐learning‐based inpainting techniques for experimental X‐ray scattering

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X‐ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U‐Nets, partial convolution neural networks and mixed‐s...

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Veröffentlicht in:	Journal of applied crystallography Jg. 55; H. 5; S. 1277 - 1288
Hauptverfasser:	Chavez, Tanny, Roberts, Eric J., Zwart, Petrus H., Hexemer, Alexander
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	5 Abbey Square, Chester, Cheshire CH1 2HU, England International Union of Crystallography 01.10.2022 Blackwell Publishing Ltd International Union of Crystallography (IUCr)
Schlagworte:	Algorithms Artificial neural networks Computer architecture Correlation coefficient Correlation coefficients Deep learning Harmonic functions image inpainting Image reconstruction Machine learning MATHEMATICS AND COMPUTING mixed-scale dense networks Neural networks Research Papers Scattering tunable U-Nets X-ray scattering
ISSN:	1600-5767, 0021-8898, 1600-5767
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Abstract	The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X‐ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U‐Nets, partial convolution neural networks and mixed‐scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground‐truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U‐Net and mixed‐scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980. A number of machine‐learning‐based algorithms are presented for the reconstruction of gaps in experimental X‐ray scattering images through inpainting approaches.
AbstractList	The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980. The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X‐ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U‐Nets, partial convolution neural networks and mixed‐scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground‐truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U‐Net and mixed‐scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980. A number of machine‐learning‐based algorithms are presented for the reconstruction of gaps in experimental X‐ray scattering images through inpainting approaches. The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980. A number of machine-learning-based algorithms are presented for the reconstruction of gaps in experimental X-ray scattering images through inpainting approaches. The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.
Author	Chavez, Tanny Hexemer, Alexander Roberts, Eric J. Zwart, Petrus H.
Author_xml	– sequence: 1 givenname: Tanny surname: Chavez fullname: Chavez, Tanny organization: Lawrence Berkeley National Laboratory – sequence: 2 givenname: Eric J. surname: Roberts fullname: Roberts, Eric J. organization: Lawrence Berkeley National Laboratory – sequence: 3 givenname: Petrus H. surname: Zwart fullname: Zwart, Petrus H. organization: Lawrence Berkeley National Laboratory – sequence: 4 givenname: Alexander surname: Hexemer fullname: Hexemer, Alexander email: ahexemer@lbl.gov organization: Lawrence Berkeley National Laboratory
BackLink	https://www.osti.gov/biblio/1890010$$D View this record in Osti.gov
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Copyright	2022 Tanny Chavez et al. published by IUCr Journals. 2022. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Tanny Chavez et al. 2022. Tanny Chavez et al. 2022 2022
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Snippet	The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X‐ray scattering data. The proposed methods use... The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use... A number of machine-learning-based algorithms are presented for the reconstruction of gaps in experimental X-ray scattering images through inpainting...
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SubjectTerms	Algorithms Artificial neural networks Computer architecture Correlation coefficient Correlation coefficients Deep learning Harmonic functions image inpainting Image reconstruction Machine learning MATHEMATICS AND COMPUTING mixed-scale dense networks Neural networks Research Papers Scattering tunable U-Nets X-ray scattering
Title	A comparison of deep‐learning‐based inpainting techniques for experimental X‐ray scattering
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