Optimal principal component Analysis of STEM XEDS spectrum images
Image and Video Processing
2019-10-16 v1 Data Analysis, Statistics and Probability
Abstract
STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.
Keywords
Cite
@article{arxiv.1910.06781,
title = {Optimal principal component Analysis of STEM XEDS spectrum images},
author = {Pavel Potapov and Axel Lubk},
journal= {arXiv preprint arXiv:1910.06781},
year = {2019}
}
Comments
21 pages, 14 figures