REFERENCES
1. Pennycook, S. J.; Jesson, D. E. High-resolution incoherent imaging of crystals. Phys. Rev. Lett. 1990, 64, 938-41.
2. Pennycook, S.; Jesson, D. Atomic resolution Z-contrast imaging of interfaces. Acta. Metallurgica. et. Materialia. 1992, 40, S149-59.
3. Qu, W.; Zhao, Z.; Yang, Y.; et al. Atomic-level quantitative analysis of electronic functional materials by aberration-corrected STEM. Chinese. Phys. B. , 33, 116802.
4. Wang, Y.; Salzberger, U.; Sigle, W.; Eren, Suyolcu. Y.; van, Aken. P. A. Oxygen octahedra picker: a software tool to extract quantitative information from STEM images. Ultramicroscopy 2016, 168, 46-52.
5. Nord, M.; Vullum, P. E.; MacLaren, I.; Tybell, T.; Holmestad, R. Atomap: a new software tool for the automated analysis of atomic resolution images using two-dimensional Gaussian fitting. Adv. Struct. Chem. Imag. 2017, 3, 9.
6. Rekik, A.; Zribi, M.; Benjelloun, M.; ben, Hamida. A. A k-Means clustering algorithm initialization for unsupervised statistical satellite image segmentation. 2006. 1ST. IEEE. International. Conference. on. E-Learning. in. Industrial. Electronics. , IEEE Publishers, Piscataway, New Jersey, USA, 2006; pp 11-6.
7. Boyat, A. K.; Joshi, B. K. A review paper: noise models in digital image processing. arXiv: arXiv:1505.03489v1 [Preprint]. 2015 [cited 2017 Feb 9]: [13 p.]. Available from: https://doi.org/10.48550/arXiv.1505.03489.
8. Cheezum, M. K.; Walker, W. F.; Guilford, W. H. Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys. J. 2001, 81, 2378-88.
9. Lin, R.; Zhang, R.; Wang, C.; Yang, X. Q.; Xin, H. L. TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images. Sci. Rep. 2021, 11, 5386.
10. Nan, H.; Lu, L.; Ma, X. Application of sliding window algorithm with convolutional neural network in high-resolution electron microscope image. J. Chin. Electr. Microsc. Soc. 2021, 40, 242-50. (in Chinese).
11. Bradley, D.; Roth, G. Adaptive thresholding using the integral image. J. Graphics. Tools. 2007, 12, 13-21.
12. Okunishi, E.; Ishikawa, I.; Sawada, H.; Hosokawa, F.; Hori, M.; Kondo, Y. Visualization of light elements at ultrahigh resolution by STEM annular bright field microscopy. Microsc. Microanal. 2009, 15, 164-5.
13. Born, M.; Wolf, E. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, 7th ed.; Cambridge University Press; 1999.
14. Hu, M. K. Visual pattern recognition by moment invariants. IRE. Trans. Inf. Theory. 1962, 8, 179-87.
15. Sneath, P. H. A. Numerical Taxonomy (by) Peter H.A. Sneath (and) Robert R. Sokal: The Principles and Practice of Numerical Classification; W.H. Freeman and Company, 1973.
17. Lewis, J. P. Fast Template Matching. In Vision Interface 95, Proceedings of the Canadian Image Processing and Pattern Recognition Society, Quebec City, Canada; May 15-19, 1995; Canadian Image Processing and Pattern Recognition Society: Québec, Canada, 1995; pp 15-9.http://scribblethink.org/Work/nvisionInterface/vi95_lewis.pdf (accessed 2025-11-26).
18. Shafait, F.; Keysers, D.; Breuel, T. M. Efficient implementation of local adaptive thresholding techniques using integral images. In IS&T/SPIE International Symposium on Electronic Imaging, Proceedings of the 15th Conference on Document Recognition and Retrieval, San Jose, USA; January 26-31, 2008; SPIE: San Jose, USA, 2008; pp 681510.
19. Frank, J.; Goldfarb, W.; Eisenberg, D.; Baker, T. S. Reconstruction of glutamine synthetase using computer averaging. Ultramicroscopy 1978, 3, 283-90.
20. Lewis,JP. Fast normalized crosscorrelation. San Rafael, CA: Industrial Light & Magic; 1995. https://www.researchgate.net/publication/2378357_Fast_Normalized_Cross-Correlation (accessed 2025-6-4).
21. Shapiro, L. G. Computer and Robot Vision; Vol 2 Reading, MA: AddisonWesley Publishing Company, 1992.
22. Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical Recipes: The Art of Scientific Computing, 3rd ed.; Cambridge University Press; 2007.
23. Nobach, H.; Honkanen, M. Two-dimensional Gaussian regression for sub-pixel displacement estimation in particle image velocimetry or particle position estimation in particle tracking velocimetry. Exp. Fluids. 2005, 38, 511-5.
24. Anthony, S. M.; Granick, S. Image analysis with rapid and accurate two-dimensional gaussian fitting. Langmuir 2009, 25, 8152-60.
25. Sigworth, F. Read .dm3 and .dm4 image files. MATLAB Central File Exchange. Accessed December 23, 2024. https://www.mathworks.com/matlabcentral/fileexchange/43005-read-dm3-and-dm4-image-files (accessed 2025-6-4).
26. Mitchell, D. R. G. Create a Synthetic HAADF Image. Version 20211129, v1.2 [Source code] http://dmscripting.com/create_a_synthetic_haadf_image.html (accessed 2025-6-4).
27. Bosman, M.; Keast, V. J.; García-Muñoz, J. L.; D'Alfonso, A. J.; Findlay, S. D.; Allen, L. J. Two-dimensional mapping of chemical information at atomic resolution. Phys. Rev. Lett. 2007, 99, 086102.
28. Mevenkamp, N.; Binev, P.; Dahmen, W.; Voyles, P. M.; Yankovich, A. B.; Berkels, B. Poisson noise removal from high-resolution STEM images based on periodic block matching. Adv. Struct. Chem. Imag. 2015, 1, 4.
29. Jones, L.; Nellist, P. D. Identifying and correcting scan noise and drift in the scanning transmission electron microscope. Microsc Microanal 2013;19:1050-60.
30. Bals, S.; Van, Aert. S.; Van, Tendeloo. G.; Avila-Brande, D. Statistical estimation of atomic positions from exit wave reconstruction with a precision in the picometer range. Phys. Rev. Lett. 2006, 96, 096106.
31. Mitchell, D. R. G. Atomic Displacement. Version 20230104, v2.1 [Source code] http://dmscripting.com/atomic_displacement.html (accessed 2025-6-4).
32. Total Resolution. *Tempas* (Version 3.0.42) [Software]. 2023. https://www.totalresolution.com/Tempas.htm.







