Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise

Ramon Manzorro, Yuchen Xu, Joshua L. Vincent, Roberto Rivera, David S. Matteson, Peter A. Crozier

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalMicroscopy and Microanalysis
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • atomic columns
  • blob detection
  • nanoparticles
  • noisy
  • time-resolved TEM
  • tracking algorithm
  • transmission electron microscopy

ASJC Scopus subject areas

  • Instrumentation

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