ArticleJournal of applied crystallography2022
A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.
Article in Journal of applied crystallography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A machine-learning-driven data labeling pipeline for scientific analysis inJournal of applied crystallography · 2025Article
- Patching-based deep-learning model for the inpainting of Bragg coherent diffraction patterns affected by detector gaps.Journal of applied crystallography · 2024Article
- DLSIA: Deep Learning for Scientific Image Analysis.Journal of applied crystallography · 2024Article
- Introduction to the virtual collection of papers onJournal of applied crystallography · 2024Article
- Article
- Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.IS&T International Symposium on Electronic Imaging · 2023Article
- MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing : XLOOP. Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing · 2022Article
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4 authors.
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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.
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