Energy-dispersive x-ray (EDX) data, multiscale modeling, robust unmixing.
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A Multiscale Bayesian Approach to Quantification and Denoising of Energy-Dispersive X-ray Data
Energy dispersive X-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. The methodology leverages Poisson statistics in the EDX data and their underlying spatial correlations, combined with spectral simulation for Bayesian estimation. Results indicate the approach produces more accurate chemical maps with higher spatial resolution than those obtained by standard methods, with applications to experimental datasets including ferroelectric domain mapping.
Multiscale Bayesian approach for EDX data quantification and denoising
P. Torruella, A. Halimi, L. Tovaglieri, C. Lichtensteiger, D. T. L. Alexander, C. Hébert, "A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data," Machine Learning: Science and Technology, 2025.
Related Publications
- P. Torruella, A. Halimi, L. Tovaglieri, C. Lichtensteiger, D. T. L. Alexander, C. Hébert, "A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data," Machine Learning: Science and Technology, 2025.