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中国物理学会期刊

同步辐射纳米CT图像配准方法研究

CSTR: 32037.14.aps.70.20210156

Image alignment for synchrotron radiation based X-ray nano-CT

CSTR: 32037.14.aps.70.20210156
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  • 基于同步辐射的X射线纳米成像技术是无损研究物质内部纳米尺度结构的强大工具, 本文总结了图像配准技术在纳米CT成像领域的研究和应用, 并根据发展阶段进行分类分析. 首先, 通过统计近年以来图像配准文献的发表情况, 分析并预测纳米尺度图像配准的未来研究方向. 其次, 基于图像经典配准算法理论, 详细介绍了图像配准算法在纳米成像领域最有效的前沿应用. 最后, 介绍了基于深度学习的图像配准方法的前沿研究, 并讨论深度学习在纳米分辨图像配准领域的适用性及发展潜能, 根据纳米尺度图像数据的特点及各种深度学习网络模型的特性, 展望了同步辐射纳米尺度图像配准技术的未来研究方向及挑战.

     

    Synchrotron radiation-based X-ray nano-imaging is a powerful tool for non-destructively studying the internal nano-scale structure of matter. Here in this paper, we review the state-of-the-art image alignment technology in the field of nano-resolution imaging, and classify and analyze the technology according to the research stage. First, through the publications of image alignment algorithm, the development direction of future research is analyzed. Then, the most effective image alignment application in the field of nano imaging based on classic image alignment algorithms is summarized. The paper also presents the feature detection operators that are useful for nano-scale image registration selected from recent feature detection research, which has important guiding significance for the specific application and optimization of nano-imaging image registration. Finally, the state-of-the-art image registration method based on deep learning is introduced, the applicability and potential of deep learning in nano-imaging registration technology are discussed, and future research directions and challenges are prospected based on different neural network characteristics.

     

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