Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling

Etienne Monier (1) , Thomas Oberlin (2, 1) , Nathalie Brun (3) , Xiaoyan Li (3) , Marcel Tencé (3) , Nicolas Dobigeon (1, 4, 5)

1 IRIT-SC - Signal et Communications
2 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
3 LPS - Laboratoire de Physique des Solides
4 IUF - Institut universitaire de France
5 Toulouse INP - Institut National Polytechnique (Toulouse)

This paper discusses the reconstruction of partially sampled spectrum-images to accelerate the acquisition in scanning transmission electron microscopy (STEM). The problem of image reconstruction has been widely considered in the literature for many imaging modalities, but only a few attempts handled 3D data such as spectral images acquired by STEM electron energy loss spectroscopy (EELS). Besides, among the methods proposed in the microscopy literature, some are fast but inaccurate while others provide accurate reconstruction but at the price of a high computation burden. Thus none of the proposed reconstruction methods fulfills our expectations in terms of accuracy and computation complexity. In this paper, we propose a fast and accurate reconstruction method suited for atomic-scale EELS. This method is compared to popular solutions such as beta process factor analysis (BPFA) which is used for the first time on STEM-EELS images. Experiments based on real as synthetic data will be conducted.

Mots clés: scanning transmission electron microscopy; electron energy loss spectroscopy; atomic-scale images; spectrum-images; partial acquisition; fast reconstruction

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Apprentissage [cs.LG]; Analyse de données, Statistiques et Probabilités [physics.data-an]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Etienne Monier, Thomas Oberlin, Nathalie Brun, Xiaoyan Li, Marcel Tencé, et al.. Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling. Ultramicroscopy, 2020, 215, pp.112993. ⟨10.1016/j.ultramic.2020.112993⟩. ⟨hal-02949156⟩