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Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling
- Source :
- Ultramicroscopy, Ultramicroscopy, Elsevier, 2020, 215, pp.112993. ⟨10.1016/j.ultramic.2020.112993⟩
- Publication Year :
- 2020
-
Abstract
- International audience; 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.
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
partial acquisition
01 natural sciences
Atomic units
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Traitement des images
Atomic-scale images
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Scanning transmission electron microscopy
Microscopy
Instrumentation
010302 applied physics
Condensed Matter - Materials Science
electron energy loss spectroscopy
Image and Video Processing (eess.IV)
Sampling (statistics)
021001 nanoscience & nanotechnology
Atomic and Molecular Physics, and Optics
Electronic, Optical and Magnetic Materials
Fast reconstruction
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
scanning transmission electron microscopy
0210 nano-technology
[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
fast reconstruction
Computation
atomic-scale images
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
FOS: Physical sciences
Iterative reconstruction
Synthetic data
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
0103 physical sciences
FOS: Electrical engineering, electronic engineering, information engineering
Partial acquisition
Electron energy loss spectroscopy
Spectrum-images
business.industry
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Materials Science (cond-mat.mtrl-sci)
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Stem eels
spectrum-images
Artificial intelligence
business
Subjects
Details
- ISSN :
- 03043991
- Database :
- OpenAIRE
- Journal :
- Ultramicroscopy
- Accession number :
- edsair.doi.dedup.....2a199c0e49c4d93aeb34617178f642f4
- Full Text :
- https://doi.org/10.1016/j.ultramic.2020.112993