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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Vydané v:	Journal of applied crystallography Ročník 55; číslo 5; s. 1277 - 1288
Hlavní autori:	Chavez, Tanny, Roberts, Eric J., Zwart, Petrus H., Hexemer, Alexander
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	5 Abbey Square, Chester, Cheshire CH1 2HU, England International Union of Crystallography 01.10.2022 Blackwell Publishing Ltd International Union of Crystallography (IUCr)
Predmet:	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
On-line prístup:	Získať plný text
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