Generative diffusion modeling protocols for improving the Kikuchi pattern indexing in electron back-scatter diffraction

TL;DR

This paper introduces generative diffusion models to enhance noisy Kikuchi patterns in electron back-scatter diffraction, enabling accurate crystal orientation indexing at high scan speeds with reduced exposure times.

Contribution

It develops and compares generative diffusion-based models for improving EBSD pattern quality, especially under low signal-to-noise conditions, with minimal data requirements.

Findings

Generative models significantly improve pattern clarity at high scan speeds.

Enhanced patterns lead to more reliable crystal orientation indexing.

Method performs well with limited training data.

Abstract

Electron back-scatter diffraction (EBSD) has traditionally relied upon methods such as the Hough transform and dictionary Indexing to interpret diffraction patterns and extract crystallographic orientation. However, these methods encounter significant limitations, particularly when operating at high scanning speeds, where the exposure time per pattern is decreased beyond the operating sensitivity of CCD camera. Hence the signal to noise ratio decreases for the observed pattern which makes the pattern noisy, leading to reduced indexing accuracy. This research work aims to develop generative machine learning models for the post-processing or on-the-fly processing of Kikuchi patterns which are capable of restoring noisy EBSD patterns obtained at high scan speeds. These restored patterns can be used for the determination of crystal orientations to provide reliable indexing results. We compare the performance of such generative models in enhancing the quality of patterns captured at short exposure times (high scan speeds). An interesting observation is that the methodology is not data-hungry as typical machine learning methods.