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愈合X射线散射图像。

Healing X-ray scattering images.

作者信息

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

机构信息

Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA.

Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, USA.

出版信息

IUCrJ. 2017 May 24;4(Pt 4):455-465. doi: 10.1107/S2052252517006212. eCollection 2017 Jul 1.

DOI:10.1107/S2052252517006212
PMID:28875032
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5571808/
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 sharp 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.

摘要

X射线散射图像包含许多由探测器局限性和实验配置引起的间隙和缺陷。我们提出了一种修复X射线散射图像的方法,以物理上有意义的方式填补数据中的间隙并去除缺陷。与一般的图像修复方法不同,该方法与倒易空间数据的预期结构紧密匹配。特别是,我们利用统计测试和对称性分析来识别图像的结构;然后,我们以尊重所识别结构和对称性的方式将测量数据复制、平均并插值到间隙中。重要的是,底层分析方法提供了图像中存在的结构的有用特征,包括识别漫射锐化特征、各向异性和对称性。所提出的方法利用了倒易空间的已知特性,即使在图像间隙较大的情况下也能实现物理上合理的重建。相应地,对于违反这些基本假设的图像,该方法将失败。该方法假设点对称性,因此适用于小角X射线散射(SAXS)数据,但仅适用于广角数据的一个子集。我们的方法成功地填补了实验图像中的间隙并修复了缺陷,包括将数据扩展到原始探测器边界之外。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/26584a9cb4e1/m-04-00455-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/41333a92914f/m-04-00455-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/a21bb1a3af60/m-04-00455-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/f186e38d0c8e/m-04-00455-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/34876c87cf95/m-04-00455-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/de96b6102a05/m-04-00455-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/26584a9cb4e1/m-04-00455-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/41333a92914f/m-04-00455-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/a21bb1a3af60/m-04-00455-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/f186e38d0c8e/m-04-00455-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/34876c87cf95/m-04-00455-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/de96b6102a05/m-04-00455-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/5571808/26584a9cb4e1/m-04-00455-fig6.jpg

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