ArticleIUCrJ2017
Healing X-ray scattering images.
Article in IUCrJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization.Chemistry of materials : a publication of the American Chemical Society · 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
- A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.Journal of applied crystallography · 2022Article
- Scanning x-ray microdiffraction: In situ molecular imaging of tissue and materials.Current opinion in structural biology · 2022Review
- Scanning structural mapping at the Life Science X-ray Scattering Beamline.Journal of synchrotron radiation · 2022Article
- Orientation mapping of graphene using 4D STEM-in-SEM.Ultramicroscopy · 2020Article
- Unwarping GISAXS data.IUCrJ · 2018Article
- Xi-cam: a versatile interface for data visualization and analysis.Journal of synchrotron radiation · 2018Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
X-ray scattering images contain numerous gaps and defects arising from detector limitations and experimental configuration. We present a method to heal X-ray scattering images, filling gaps in the data and removing defects in a physically meaningful manner. Unlike generic inpainting methods, this method is closely tuned to the expected structure of reciprocal-space data. In particular, we exploit statistical tests and symmetry analysis to identify the structure of an image; we then copy, average and interpolate measured data into gaps in a way that respects the identified structure and symmetry. Importantly, the underlying analysis methods provide useful characterization of structures present in the image, including the identification of diffuse
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.