Denoising an X-ray image by exploring the power of its physical symmetry

IF 5.2 3区 材料科学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Zhongzheng Zhou, Chun Li, Longlong Fan, Zheng Dong, Wenhui Wang, Chen Liu, Bingbing Zhang, Xiaoyan Liu, Kai Zhang, Ling Wang, Yi Zhang, Yuhui Dong
{"title":"Denoising an X-ray image by exploring the power of its physical symmetry","authors":"Zhongzheng Zhou,&nbsp;Chun Li,&nbsp;Longlong Fan,&nbsp;Zheng Dong,&nbsp;Wenhui Wang,&nbsp;Chen Liu,&nbsp;Bingbing Zhang,&nbsp;Xiaoyan Liu,&nbsp;Kai Zhang,&nbsp;Ling Wang,&nbsp;Yi Zhang,&nbsp;Yuhui Dong","doi":"10.1107/S1600576724002899","DOIUrl":null,"url":null,"abstract":"<p>Next-generation light source facilities offer extreme spatial and temporal resolving power, enabling multiscale, ultra-fast and dynamic characterizations. However, a trade-off between acquisition efficiency and data quality needs to be made to fully unleash the resolving potential, for which purpose powerful denoising algorithms to improve the signal-to-noise ratio of the acquired X-ray images are desirable. Yet, existing models based on machine learning mostly require massive and diverse labeled training data. Here we introduce a self-supervised pre-training algorithm with blind denoising capability by exploring the intrinsic physical symmetry of X-ray patterns without requiring high signal-to-noise ratio reference data. The algorithm is more efficient and effective than algorithms without symmetry involved, including an supervised algorithm. It allows us to recover physical information from spatially and temporally resolved data acquired in X-ray diffraction/scattering and pair distribution function experiments, where pattern symmetry is often well preserved. This study facilitates photon-hungry experiments as well as <i>in situ</i> experiments with dynamic loading.</p>","PeriodicalId":48737,"journal":{"name":"Journal of Applied Crystallography","volume":"57 3","pages":"741-754"},"PeriodicalIF":5.2000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Crystallography","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1107/S1600576724002899","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Next-generation light source facilities offer extreme spatial and temporal resolving power, enabling multiscale, ultra-fast and dynamic characterizations. However, a trade-off between acquisition efficiency and data quality needs to be made to fully unleash the resolving potential, for which purpose powerful denoising algorithms to improve the signal-to-noise ratio of the acquired X-ray images are desirable. Yet, existing models based on machine learning mostly require massive and diverse labeled training data. Here we introduce a self-supervised pre-training algorithm with blind denoising capability by exploring the intrinsic physical symmetry of X-ray patterns without requiring high signal-to-noise ratio reference data. The algorithm is more efficient and effective than algorithms without symmetry involved, including an supervised algorithm. It allows us to recover physical information from spatially and temporally resolved data acquired in X-ray diffraction/scattering and pair distribution function experiments, where pattern symmetry is often well preserved. This study facilitates photon-hungry experiments as well as in situ experiments with dynamic loading.

Abstract Image

通过探索物理对称性的力量对 X 射线图像去噪
下一代光源设备具有极强的空间和时间分辨能力,可进行多尺度、超快速和动态表征。然而,要充分发挥分辨潜力,需要在采集效率和数据质量之间做出权衡,为此,我们需要功能强大的去噪算法来提高所采集 X 射线图像的信噪比。然而,现有的基于机器学习的模型大多需要大量、多样的标注训练数据。在这里,我们通过探索 X 射线模式的内在物理对称性,引入了一种具有盲去噪能力的自监督预训练算法,而无需高信噪比的参考数据。与不涉及对称性的算法(包括监督算法)相比,该算法更加高效和有效。它使我们能够从 X 射线衍射/散射和成对分布函数实验中获取的空间和时间分辨数据中恢复物理信息,在这些实验中,图案的对称性通常保存得很好。这项研究为高光子消耗实验以及动态加载的原位实验提供了便利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Applied Crystallography
Journal of Applied Crystallography CHEMISTRY, MULTIDISCIPLINARYCRYSTALLOGRAPH-CRYSTALLOGRAPHY
CiteScore
7.80
自引率
3.30%
发文量
178
期刊介绍: Many research topics in condensed matter research, materials science and the life sciences make use of crystallographic methods to study crystalline and non-crystalline matter with neutrons, X-rays and electrons. Articles published in the Journal of Applied Crystallography focus on these methods and their use in identifying structural and diffusion-controlled phase transformations, structure-property relationships, structural changes of defects, interfaces and surfaces, etc. Developments of instrumentation and crystallographic apparatus, theory and interpretation, numerical analysis and other related subjects are also covered. The journal is the primary place where crystallographic computer program information is published.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信