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Local low rank denoising for enhanced atomic resolution imaging
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.ORCID iD: 0000-0002-6550-0087
Oak Ridge National Laboratory, Center for Nanophase Materials Sciences, Oak Ridge, TN 37831, USA.
Oak Ridge National Laboratory, Materials Sciences and Technology Division, Oak Ridge, TN 37831, USA.
Oak Ridge National Laboratory, Materials Sciences and Technology Division, Oak Ridge, TN 37831, USA.
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2018 (English)In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 187, p. 34-42Article in journal (Refereed) Published
Abstract [en]

Atomic resolution imaging and spectroscopy suffers from inherently low signal to noise ratios often prohibiting the interpretation of single pixels or spectra. We introduce local low rank (LLR) denoising as tool for efficient noise removal in scanning transmission electron microscopy (STEM) images and electron energy-loss (EEL) spectrum images. LLR denoising utilizes tensor decomposition techniques, in particular the multilinear singular value decomposition (MLSVD), to achieve a denoising in a general setting largely independent of the signal features and data dimension, by assuming that the signal of interest is of low rank in segments of appropriately chosen size. When applied to STEM images of graphene, LLR denoising suppresses statistical noise while retaining fine image features such as scan row-wise distortions, possibly related to rippling of the graphene sheet and consequent motion of atoms. When applied to EEL spectra, LLR denoising reveals fine structures distinguishing different lattice sites in the spinel system CoFe2O4.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 187, p. 34-42
National Category
Atom and Molecular Physics and Optics
Identifiers
URN: urn:nbn:se:uu:diva-348246DOI: 10.1016/j.ultramic.2018.01.012ISI: 000428131200005OAI: oai:DiVA.org:uu-348246DiVA, id: diva2:1196957
Available from: 2018-04-11 Created: 2018-04-11 Last updated: 2018-06-04Bibliographically approved
In thesis
1. Signal Processing Tools for Electron Microscopy
Open this publication in new window or tab >>Signal Processing Tools for Electron Microscopy
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The detection of weak signals in noisy data is a problem which occurs across various disciplines. Here, the signal of interest is the spectral signature of the electron magnetic chiral dichroism (EMCD) effect. In principle, EMCD allows for the measurement of local magnetic structures in the electron microscope, its spatial resolution, versatility and low hardware requirements giving it an eminent position among competing measurement techniques. However, experimental shortcomings as well as intrinsically low signal to noise ratio render its measurement challenging to the present day.   

This thesis explores how posterior data processing may aid the analysis of various data from the electron microscope. Following a brief introduction to different signals arising in the microscope and a yet briefer survey of the state of the art of EMCD measurements, noise removal strategies are presented. Afterwards, gears are shifted to discuss the separation of mixed signals into their physically meaningful source components based on their assumed mathematical characteristics, so called blind source separation (BSS).    

A data processing workflow for detecting weak signals in noisy spectra is derived from these considerations, ultimately culminating in several demonstrations of the extraction of EMCD signals. While the focus of the thesis does lie on data processing strategies for EMCD detection, the approaches presented here are similarly applicable in other situations. Related topics such as the general analysis of hyperspectral images using BSS methods or the fast analysis of large data sets are also discussed.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 60
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1672
National Category
Physical Sciences Computer Sciences Other Mathematics
Identifiers
urn:nbn:se:uu:diva-348264 (URN)978-91-513-0345-1 (ISBN)
Public defence
2018-06-12, Å2001, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-04-11 Last updated: 2018-05-18

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Spiegelberg, JakobRusz, Ján

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