Assessment of speckle-pattern quality in digital image correlation based on gray intensity and speckle morphology

Jihyuk Park, Sungsik Yoon, Tae Hyun Kwon, Kyoungsoo Park

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68 Citations (Scopus)


In digital image correlation (DIC), speckle patterns are generated on the surface of a specimen to resolve uniqueness issues. Thus, speckle patterns significantly affect the accuracy of image correlation. To assess the quality of speckle patterns, the standard deviation of gray intensities within each speckle (SDGIS) is introduced as a new metric. On the basis of the cumulative distribution of SDGIS, speckle-pattern quality measurement (ρ) is proposed, which integrates the features of gray intensity and speckle morphology. Twelve speckle patterns are generated by changing the spraying time and nozzle sizes of an airbrush because these are associated with the speckle volume fraction and speckle size, respectively. In addition, three displacement fields are used to investigate the effects of speckle patterns on the accuracy of the DIC results. For the 12 speckle images associated with the three displacement fields, the correlation results demonstrate that the proposed speckle-pattern quality measurement is inversely proportional to the averaged error of the subset method. This is statistically confirmed by evaluating the correlation coefficient and p-value. Furthermore, the error of the subset method is more affected by speckle patterns than the subset size when the subset size is sufficiently large.

Original languageEnglish
Pages (from-to)62-72
Number of pages11
JournalOptics and Lasers in Engineering
Publication statusPublished - 2017 Apr 1

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program received through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning ( NRF-2015R1C1A1A02037663 ). The information presented in this paper represents the opinions and views of the authors and does not necessarily reflect the views of the sponsoring agencies.

Publisher Copyright:
© 2016 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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