Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise
Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior...
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          | Published in | Microscopy and microanalysis Vol. 28; no. 6; pp. 1917 - 1930 | 
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| Main Authors | , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        New York, USA
          Cambridge University Press
    
        01.12.2022
     Oxford University Press  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1431-9276 1435-8115 1435-8115  | 
| DOI | 10.1017/S1431927622000356 | 
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| Summary: | Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision. | 
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23  | 
| ISSN: | 1431-9276 1435-8115 1435-8115  | 
| DOI: | 10.1017/S1431927622000356 |