# Prediction of microstructural representativity from a single image

arXiv: https://arxiv.org/abs/2410.19568 · HTML: https://mlsanity.com/paper/2410.19568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Generating 3D structures from a 2D slice with GAN-based dimensionality expansion](https://mlsanity.com/paper/2102.07708/) (2021)
- [MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN](https://mlsanity.com/paper/2210.06541/) (2022)
- [SAMBA: A Trainable Segmentation Web-App with Smart Labelling](https://mlsanity.com/paper/2312.04197/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks](https://mlsanity.com/paper/2608.19117/) (2026)

## Abstract (arXiv)

In this study, we present a method for predicting the representativity of the phase fraction observed in a single image (2D or 3D) of a material. Traditional approaches often require large datasets and extensive statistical analysis to estimate the Integral Range, a key factor in determining the variance of microstructural properties. Our method leverages the Two-Point Correlation function to directly estimate the variance from a single image, thereby enabling phase fraction prediction with associated confidence levels. We validate our approach using open-source datasets, demonstrating its efficacy across diverse microstructures. This technique significantly reduces the data requirements for representativity analysis, providing a practical tool for material scientists and engineers working with limited microstructural data. To make the method easily accessible, we have created a web-application, www.imagerep.io, for quick, simple and informative use of the method.