Noise Reduction of Low Count STEM-EDX Data by Low-rank Regularized Spectral Smoothing

15 September 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of wPCA without preprocessing, such as spatial averaging, often fails against low count STEM-EDX data. To enhance the applicability of wPCA while retaining spatial resolution, a step-by-step noise reduction method is considered in this study. Specifically, a numerical optimization is developed to simultaneously characterize the smoothness of EDX spectra and the low-rankness of the data. In the presented approach, a low count data is first spectrally smoothed by solving this optimization problem, and then further denoised by using wPCA to project onto a subspace rigorously spanned by a small number of components. A challenging example is provided, and the improved noise reduction performance is demonstrated and compared to using existing spectral smoothing techniques.


Data analysis
Noise reduction
Principal component analysis
Machine learning


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