HAADF STEM图像原子柱检测的最大后验概率准则
The maximum a posteriori probability rule for atom column detection from HAADF STEM images
摘要：
最近，最大后验概率（MAP）规则已被提出作为一个客观和定量的方法来检测原子柱，甚至单原子从高分辨率高角度环形暗场（HAADF）扫描透射电子显微镜（STEM）图像。该方法结合了统计参数估计和模型阶次选择使用贝叶斯框架，并已被证明是特别有用的光束敏感纳米材料的结构分析。为了避免束损伤，通常使用有限的入射电子剂量来获取此类材料的图像，从而导致低对比度噪声比（CNR），这使得目视检查不可靠。这就需要采取一种客观和定量的办法。本文描述了MAP概率规则的方法，给出了它的逐步推导过程，并讨论了它在原子柱检测中的算法实现。此外，模拟结果表明，MAP概率规则的性能，以检测正确的原子列数从HAADF STEM图像是优于其他模型顺序选择标准，包括赤池信息准则（AIC）和贝叶斯信息准则（BIC）。此外，MAP概率规则被用作评估STEM图像质量测量和原子可检测性之间的关系的工具，从而引入所谓的集成CNR（ICNR）作为一种新的图像质量测量，其比常规测量（如信噪比（SNR）和CNR）更好地与原子可检测性相关。
英文摘要：
Recently, the maximum a posteriori (MAP) probability rule has been proposed as an objective and quantitative method to detect atom columns and even single atoms from high-resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images. The method combines statistical parameter estimation and model-order selection using a Bayesian framework and has been shown to be especially useful for the analysis of the structure of beam-sensitive nanomaterials. In order to avoid beam damage, images of such materials are usually acquired using a limited incoming electron dose resulting in a low contrast-to-noise ratio (CNR) which makes visual inspection unreliable. This creates a need for an objective and quantitative approach. The present paper describes the methodology of the MAP probability rule, gives its step-by-step derivation and discusses its algorithmic implementation for atom column detection. In addition, simulation results are presented showing that the performance of the MAP probability rule to detect the correct number of atomic columns from HAADF STEM images is superior to that of other model-order selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Moreover, the MAP probability rule is used as a tool to evaluate the relation between STEM image quality measures and atom detectability resulting in the introduction of the so-called integrated CNR (ICNR) as a new image quality measure that better correlates with atom detectability than conventional measures such as signal-to-noise ratio (SNR) and CNR.
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