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HAADF-STEM Image Resolution Enhancement Using High-Quality Image Reconstruction Techniques: Case of the Fe3O4(111) Surface

Published online by Cambridge University Press:  13 August 2019

G. Bárcena-González Open the ORCID record for G. Bárcena-González [Opens in a new window] ,
M. P. Guerrero-Lebrero ,
E. Guerrero ,
A. Yañez ,
B. Nuñez-Moraleda ,
D. Kepaptsoglou ,
V. K. Lazarov  and
P. L. Galindo
G. Bárcena-González*
Affiliation:
Department of Computer Science and Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
M. P. Guerrero-Lebrero
Affiliation:
Department of Computer Science and Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
E. Guerrero
Affiliation:
Department of Computer Science and Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
A. Yañez
Affiliation:
Department of Computer Science and Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
B. Nuñez-Moraleda
Affiliation:
Department of Computer Science and Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
D. Kepaptsoglou
Affiliation:
Department of Physics, University of York, Heslington, York, UK SuperSTEM Laboratory, SciTech Daresbury Campus, Daresbury WA4 4AD, UK
V. K. Lazarov
Affiliation:
Department of Physics, University of York, Heslington, York, UK
P. L. Galindo
Affiliation:
Department of Computer Science and Engineering, Universidad de Cádiz, 11510 Puerto Real, Spain
*
*Author for correspondence: G. Bárcena-González, E-mail: [email protected]
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Abstract

From simple averaging to more sophisticated registration and restoration strategies, such as super-resolution (SR), there exist different computational techniques that use a series of images of the same object to generate enhanced images where noise and other distortions have been reduced. In this work, we provide qualitative and quantitative measurements of this enhancement for high-angle annular dark-field scanning transmission electron microscopy imaging. These images are compared in two ways, qualitatively through visual inspection in real and reciprocal space, and quantitatively, through the calculation of objective measurements, such as signal-to-noise ratio and atom column roundness. Results show that these techniques improve the quality of the images. In this paper, we use an SR methodology that allows us to take advantage of the information present in the image frames and to reliably facilitate the analysis of more difficult regions of interest in experimental images, such as surfaces and interfaces. By acquiring a series of cross-sectional experimental images of magnetite (Fe3O4) thin films (111), we have generated interpolated images using averaging and SR, and reconstructed the atomic structure of the very top surface layer that consists of a full monolayer of Fe, with topmost Fe atoms in tetrahedrally coordinated sites.

Keywords

alignmentdrift distortionHAADF imagesimage disturbancesreconstructionscan noise
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 6 , December 2019 , pp. 1297 - 1303
Copyright
Copyright © Microscopy Society of America 2019 

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References

ASME 14.5M (1994). Dimensional and Tolerancing Professional Certification. The American Society of Mechanical Engineers. pp. 250. https://scholar.google.com/scholar_lookup?hl=en&publication_year=1994&author=ASME+Standard&title=Dimensioning+and+Tolerancing.Google Scholar
Barbieri, A, Weiss, W, Van Hove, MA & Somorjai, GA (1994). Magnetite Fe3O4 (111): Surface structure by LEED crystallography and energetics. Surf Sci 302(3), 259–279.Google Scholar
Bárcena-González, G, Guerrero-Lebrero, MP, Guerrero, E, Fernández-Reyes, D, González, D, Mayoral, A, Utrilla, AD, Ulloa, JM & Galindo, PL (2016). Strain mapping accuracy improvement using super-resolution techniques. J Microsc 262(1), 50–58. http://doi.org/10.1111/jmi.12341Google Scholar
Bárcena-González, G, Guerrero-Lebrero, MP, Guerrero, E, Yañez, A, Fernández-Reyes, D, González, D & Galindo, PL (2017). Evaluation of high-quality image reconstruction techniques applied to high-resolution Z-contrast imaging. Ultramicroscopy 182, 283–291. http://doi.org/10.1016/j.ultramic.2017.07.014Google Scholar
Berkels, B, Binev, P, Blom, DA, Dahmen, W, Sharpley, RC & Vogt, T (2014). Optimized imaging using non-rigid registration. Ultramicroscopy 138, 46–56. http://doi.org/10.1016/j.ultramic.2013.11.007Google Scholar
Berkels, B, Yankovich, AB, Shi, F, Voyles, PM, Dahmen, W, Sharpley, R & Binev, P (2012). High precision STEM imaging by non-rigid alignment and averaging of a series of short exposures. Microsc Microanal 18(S2), 300–301. http://doi.org/10.1017/S1431927612003352Google Scholar
Binev, P, Blanco-Silva, F, Blom, D, Dahmen, W, Lamby, P, Sharpley, R, & Vogt, T (2012). High-quality image formation by nonlocal means applied to high-angle annular dark-field scanning transmission electron microscopy (HAADF–STEM). Model Nanoscale Imaging Electron Microsc 127–145. http://doi.org/10.1007/978-1-4614-2191-7_5Google Scholar
Braidy, N, Le Bouar, Y, Lazar, S & Ricolleau, C (2012 a). Correcting scanning instabilities from images of periodic structures. Ultramicroscopy 118, 67–76. http://doi.org/10.1016/j.ultramic.2012.04.001Google Scholar
Braidy, N, Le Bouar, Y, Lazar, S & Ricolleau, C (2012 b). Instabilities in scanning probe images of periodic structures: Detection and corrections. Microsc Microanal 18(S2), 378–379.Google Scholar
Buades, A., Coll, B., & Morel, J. M. (2005). A non-local algorithm for image denoising. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65. http://doi.org/10.1109/CVPR.2005.38Google Scholar
Hovden, R, Jiang, Y, Xin, HL & Kourkoutis, LF (2015). Periodic artifact reduction in Fourier transforms of full field atomic resolution images. Microsc Microanal 21(2), 436–441. http://doi.org/10.1017/S1431927614014639Google Scholar
Jones, L & Nellist, PD (2013). Identifying and correcting scan noise and drift in the scanning transmission electron microscope. Microsc Microanal 19(4), 1050–1060. http://doi.org/10.1017/S1431927613001402Google Scholar
Jones, L, Yang, H, Pennycook, TJ, Marshall, MSJ, Van Aert, S, Browning, ND, Castell, MR & Nellist, PD (2015). Smart align—a new tool for robust non-rigid registration of scanning microscope data. Adv Struct Chem Imaging 1(1), 8. http://doi.org/10.1186/s40679-015-0008-4Google Scholar
Kang, MG & Chaudhuri, S (2003). Super-resolution image reconstruction. IEEE Signal Process Mag 20(3), 19–20. http://doi.org/10.1109/MSP.2003.1203206Google Scholar
Kimoto, K, Asaka, T, Yu, X, Nagai, T, Matsui, Y & Ishizuka, K (2010). Local crystal structure analysis with several picometer precision using scanning transmission electron microscopy. Ultramicroscopy 110(7), 778–782. http://doi.org/10.1016/j.ultramic.2009.11.014Google Scholar
Lemire, C, Meyer, R, Henrich, VE, Shaikhutdinov, S & Freund, H-J (2004). The surface structure of Fe3O4 (1 1 1) films as studied by CO adsorption. Surf Sci 572(1), 103–114.Google Scholar
Mevenkamp, N, Binev, P, Dahmen, W, Voyles, PM, Yankovich, AB & Berkels, B (2015). Poisson noise removal from high-resolution STEM images based on periodic block matching. Adv Struct Chem Imaging 1(1), 3. http://doi.org/10.1186/s40679-015-0004-8Google Scholar
Moisan, L (2011). Periodic plus smooth image decomposition. J Math Imaging Vis 39(2), 161–179. http://doi.org/10.1007/s10851-010-0227-1Google Scholar
Nakanishi, N, Yamazaki, T, Rečnik, A, Čeh, M, Kawasaki, M, Watanabe, K & Shiojiri, M (2002). Retrieval process of high-resolution HAADF-STEM images. J Electron Microsc 51(6), 383–390. http://doi.org/10.1093/jmicro/51.6.383Google Scholar
Nasrollahi, K & Moeslund, TB (2014). Super-resolution: A comprehensive survey. Mach Vis Appl 25, 1423–1468. http://doi.org/10.1007/s00138-014-0623-4Google Scholar
Noh, J, Osman, OI, Aziz, SG, Winget, P & Brédas, J-L (2015). Magnetite Fe3O4 (111) surfaces: Impact of defects on structure, stability, and electronic properties. Chem Mater 27(17), 5856–5867.Google Scholar
Ophus, C, Ciston, J & Nelson, CT (2016). Correcting nonlinear drift distortion of scanning probe and scanning transmission electron microscopies from image pairs with orthogonal scan directions. Ultramicroscopy 162, 1–9. http://doi.org/10.1016/j.ultramic.2015.12.002Google Scholar
Protter, M, Elad, M, Member, S, Takeda, H & Member, S (2009). Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans Image Process 18(1), 36–51.Google Scholar
Rečnik, A, Möbus, G & Šturm, S (2005). IMAGE-WARP: A real-space restoration method for high-resolution STEM images using quantitative HRTEM analysis. Ultramicroscopy 103(4), 285–301. http://doi.org/10.1016/j.ultramic.2005.01.003Google Scholar
Saito, M, Kimoto, K, Nagai, T, Fukushima, S, Akahoshi, D, Kuwahara, H, Matsui, Y & Ishizuka, K (2009). Local crystal structure analysis with 10-pm accuracy using scanning transmission electron microscopy. J Electron Microsc 58(3), 131–136. http://doi.org/10.1093/jmicro/dfn023Google Scholar
Samuel, GL & Shunmugam, MS (2003). Evaluation of circularity and sphericity from coordinate measurement data. J Mater Process Technol 139(1–3 Spec), 90–95. http://doi.org/10.1016/S0924-0136(03)00187-0.Google Scholar
Sang, X & LeBeau, JM (2014). Revolving scanning transmission electron microscopy: Correcting sample drift distortion without prior knowledge. Ultramicroscopy 138, 28–35. http://doi.org/10.1016/j.ultramic.2013.12.004Google Scholar
Simonyan, K, Grishin, S, Vatolin, D, & Popov, D (2008). Fast video super-resolution via classification. In Proceedings-International Conference on Image Processing, ICIP, pp. 349–352. http://doi.org/10.1109/ICIP.2008.4711763Google Scholar
Sui, W & Zhang, D (2012). Four methods for roundness evaluation. Phys Procedia 24, 2159–2164. http://doi.org/10.1016/j.phpro.2012.02.317.Google Scholar
Tao, Y, Wen, X-D, Jun, REN, Li, Y-W, Wang, J-G & Huo, C-F (2010). Surface structures of Fe3O4 (111),(110), and (001): A density functional theory study. J Fuel Chem Technol 38(1), 121–128.Google Scholar
Vandewalle, P, Süsstrunk, S & Vetterll, M (2006). A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP J Appl Signal Process 2006, 1–14. http://doi.org/10.1155/ASP/2006/71459Google Scholar
Wang, Y, Suyolcu, YE, Salzberger, U, Hahn, K, Srot, V, Sigle, W & Van Aken, PA (2018). Correcting the linear and nonlinear distortions for atomically resolved STEM spectrum and diffraction imaging. Microscopy 67(suppl_1), i114–i122. http://doi.org/10.1093/jmicro/dfy002Google Scholar
Watanabe, K, Kotaka, Y, Nakanishi, N, Yamazaki, T, Hashimoto, I & Shiojiri, M (2002). Deconvolution processing of HAADF STEM images. Ultramicroscopy 92, 191–199.Google Scholar
Weiss, W & Ranke, W (2002). Surface chemistry and catalysis on well-defined epitaxial iron-oxide layers. Prog Surf Sci 70(1–3), 1–151.Google Scholar
Xiuming, L & Jingcai, Z (2014). Evaluation for the minimum circumscribed circle based on the rotation method. Meas Sci Technol 25(9), 017002. http://doi.org/10.1088/0957-0233/25/9/097001Google Scholar
Yang, J & Huang, T (2010). Image super-resolution: Historical overview and future challenges. In Super-Resolution Imaging, Milanfar, P (Ed.), pp. 3–25. CRC Press (Taylor & and amp and Francis Group).Google Scholar
Yankovich, AB, Berkels, B, Dahmen, W, Binev, P & Voyles, PM (2015). High-precision scanning transmission electron microscopy at coarse pixel sampling for reduced electron dose. Adv Struct Chem Imaging 1(1), 2. http://doi.org/10.1186/s40679-015-0003-9Google Scholar
Yu, X, Huo, C-F, Li, Y-W, Wang, J & Jiao, H (2012). Fe3O4 surface electronic structures and stability from GGA+ U. Surf Sci 606(9–10), 872–879.Google Scholar