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Liu J, Lhermitte J, Tian Y, et al. Healing X-ray scattering images. IUCrJ. 2017;4:455-465doi: 10.1107/S2052252517006212.
Liu, J., Lhermitte, J., Tian, Y., Zhang, Z., Yu, D., & Yager, K. G. (2017). Healing X-ray scattering images. IUCrJ, 4455-465. https://doi.org/10.1107/S2052252517006212
Liu, Jiliang, et al. "Healing X-ray scattering images." IUCrJ vol. 4 (2017): 455-465. doi: https://doi.org/10.1107/S2052252517006212
Liu J, Lhermitte J, Tian Y, Zhang Z, Yu D, Yager KG. Healing X-ray scattering images. IUCrJ. 2017 May 24;4:455-465. doi: 10.1107/S2052252517006212. eCollection 2017 Jul 01. PMID: 28875032; PMCID: PMC5571808.
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IUCrJ. 2017 May 24;4:455-465. doi: 10.1107/S2052252517006212. eCollection 2017 Jul 01.

Healing X-ray scattering images.

IUCrJ

Jiliang Liu, Julien Lhermitte, Ye Tian, Zheng Zhang, Dantong Yu, Kevin G Yager

Affiliations

  1. Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA.
  2. Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, USA.
  3. New Jersey Institute of Technology, Newark, New Jersey 07102, USA.

PMID: 28875032 PMCID: PMC5571808 DOI: 10.1107/S2052252517006212

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

Keywords: SAXS; X-ray scattering; data completion; image healing; inpainting

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