Department of Materials Science and Engineering, McMaster University, Hamilton L8S 4L7, Canada.
Micron. 2012 Jan;43(1):57-67. doi: 10.1016/j.micron.2011.07.008. Epub 2011 Jul 19.
In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.
在这项工作中，我们研究了原子分辨率电子能量损失谱图像（SI）数据的统计处理和背景拟合方法。分析了主成分分析（PCA）在 SI 数据中的应用，从光谱信噪比（SNR）的角度进行了分析，发现它提高了 SI 的光谱 SNR 和其标准差，尽管只有后者在所有分析的数据集中都显著且一致地提高了。分析了在重建数据集中小波包数量对 SNR 和元素图的影响，并将实验结果与理论计算进行了比较。
