Potapov Pavel
Leibniz Institute for Solid State and Material Research, Helmholtzstraße 20, 01069 Dresden, Germany.
Ultramicroscopy. 2017 Nov;182:191-194. doi: 10.1016/j.ultramic.2017.06.023. Epub 2017 Jul 6.
Principal Component Analysis (PCA) can drastically denoise STEM spectrum-images but might distort or cut off the important variations in data. The present paper analyzes various approaches to estimate such deviations and compares them with the simulated data. A spiked covariance model by Nadler (2008) appears to be most appropriated for application in STEM spectrum-imaging.
主成分分析（PCA）可以大幅去除扫描透射电子显微镜（STEM）光谱图像中的噪声，但可能会扭曲或截断数据中的重要变化。本文分析了估计此类偏差的各种方法，并将它们与模拟数据进行比较。Nadler（2008年）提出的尖峰协方差模型似乎最适合应用于STEM光谱成像。
