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Title: Healing X-ray scattering images

X-ray scattering images contain numerous gaps and defects arising from detector limitations and experimental configuration. Here, 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 diffuseversussharp features, anisotropy and symmetry. The presented method leverages known characteristics of reciprocal space, enabling physically reasonable reconstruction even with large image gaps. The method will correspondingly fail for images that violate these underlying assumptions. The method assumes point symmetry and is thus applicable to small-angle X-ray scattering (SAXS) data, but only to a subset of wide-angle data. Our method succeeds in filling gaps and healing defects in experimental images, including extending data beyond the original detector borders.
Authors:
ORCiD logo [1] ; ORCiD logo [1] ;  [1] ;  [1] ;  [2] ;  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States). Center for Functional Nanomaterials (CFN)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States). Computational Science Center; New Jersey Inst. of Technology, Newark, NJ (United States)
Publication Date:
Report Number(s):
BNL-114830-2017-JAAM
Journal ID: ISSN 2052-2525; IUCRAJ
Grant/Contract Number:
SC0012704
Type:
Accepted Manuscript
Journal Name:
IUCrJ
Additional Journal Information:
Journal Volume: 4; Journal Issue: 4; Journal ID: ISSN 2052-2525
Publisher:
International Union of Crystallography
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
Language:
English
Subject:
77 NANOSCIENCE AND NANOTECHNOLOGY
OSTI Identifier:
1425008

Liu, Jiliang, Lhermitte, Julien, Tian, Ye, Zhang, Zheng, Yu, Dantong, and Yager, Kevin G. Healing X-ray scattering images. United States: N. p., Web. doi:10.1107/S2052252517006212.
Liu, Jiliang, Lhermitte, Julien, Tian, Ye, Zhang, Zheng, Yu, Dantong, & Yager, Kevin G. Healing X-ray scattering images. United States. doi:10.1107/S2052252517006212.
Liu, Jiliang, Lhermitte, Julien, Tian, Ye, Zhang, Zheng, Yu, Dantong, and Yager, Kevin G. 2017. "Healing X-ray scattering images". United States. doi:10.1107/S2052252517006212. https://www.osti.gov/servlets/purl/1425008.
@article{osti_1425008,
title = {Healing X-ray scattering images},
author = {Liu, Jiliang and Lhermitte, Julien and Tian, Ye and Zhang, Zheng and Yu, Dantong and Yager, Kevin G.},
abstractNote = {X-ray scattering images contain numerous gaps and defects arising from detector limitations and experimental configuration. Here, 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 diffuseversussharp features, anisotropy and symmetry. The presented method leverages known characteristics of reciprocal space, enabling physically reasonable reconstruction even with large image gaps. The method will correspondingly fail for images that violate these underlying assumptions. The method assumes point symmetry and is thus applicable to small-angle X-ray scattering (SAXS) data, but only to a subset of wide-angle data. Our method succeeds in filling gaps and healing defects in experimental images, including extending data beyond the original detector borders.},
doi = {10.1107/S2052252517006212},
journal = {IUCrJ},
number = 4,
volume = 4,
place = {United States},
year = {2017},
month = {5}
}