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Robust surface-correlation-function evaluation from experimental discrete digital images.

Aleksei Samarin1,2, Vasily Postnicov1, Marina V Karsanina1

  • 1Schmidt Institute of Physics of the Earth of Russian Academy of Sciences, Moscow 107031, Russia.

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This study introduces a digital method using edge-detecting filters to calculate surface correlation functions (CFs) for porous media from images. This approach accurately characterizes porous material structures, even with lower-resolution imaging data.

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Area of Science:

  • Materials Science and Engineering
  • Physics of Complex Systems
  • Image Analysis and Computational Imaging

Background:

  • Correlation functions (CFs) are vital for describing material structures, with surface-specific CFs offering insights into interfaces.
  • Existing continuous methods for surface CFs are limited by image artifacts in digital porous media analysis (e.g., from X-ray tomography).
  • Accurate characterization of solid-fluid interfaces in porous materials is crucial for understanding their properties.

Purpose of the Study:

  • To develop a digital method for computing surface correlation functions directly from 2D and 3D images of porous media.
  • To address limitations of continuous methods when dealing with digital images affected by partial volume effects and density variations.
  • To provide a robust framework for analyzing porous material structures using experimental image data.

Main Methods:

  • Employed edge-detecting filters to compute surface-surface (Fss) and surface-void (Fsv) CFs directly on digital images.
  • Developed a C0.5 criterion based on multiscale image analysis to assess imaging resolution adequacy for accurate CF evaluation.
  • Utilized image magnification to improve CF accuracy for images not initially meeting the C0.5 criterion.

Main Results:

  • The digital method accurately computes surface CFs, matching analytical results for ideal cases (e.g., Poisson disks) at sufficient resolution.
  • The C0.5 criterion effectively predicts the reliability of surface CF calculations from digital images.
  • Image magnification can compensate for lower resolution, enabling accurate CF estimation when major structural features are present.

Conclusions:

  • The proposed digital approach provides a versatile and accurate method for calculating surface CFs from experimental images of porous media.
  • This methodology enhances structural analysis, stochastic reconstructions, super-resolution techniques, and serves as an efficient metric for machine learning.
  • The open-source computational framework (CorrelationFunctions.jl) facilitates broader application in materials science and related fields.