Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise
Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise
复制标题
探索斑点检测以确定具有超低信噪比的时间分辨 TEM 图像中的原子柱位置和强度
DOI：
10.1017/s1431927622000356
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发表时间：
2022
影响因子：
2.8
通讯作者：
Crozier, Peter A.
中科院分区：
文献类型：
--
作者：
Manzorro, Ramon;Xu, Yuchen;Vincent, Joshua L.;Rivera, Roberto;Matteson, David S.;Crozier, Peter A.
配备直接电子探测系统的空间分辨原位透射电子显微镜（TEM）是一种合适的技术，可以从材料中以毫秒级的时间分辨率记录原子尺度动力学信息。然而，表征动态或流动行为需要处理通常具有严重退化的信噪比的短时间曝光图像。与高时间分辨率相关的差的信噪比使得确定经历结构动力学的材料中原子柱的位置和强度具有挑战性。为了应对这一挑战，我们提出了一种基于斑点检测的噪声鲁棒性处理方法，该方法先前已被建立用于在计算机视觉社区中识别图像中的对象。特别是，斑点检测算法已被定制来处理来自纳米颗粒系统的噪声TEM图像系列。在高噪声含量的存在下，我们的斑点检测方法被证明优于其他算法的结果，使原子柱的位置和强度的测定具有更高的精度。
Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision.
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