Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data

TL;DR

This paper introduces a multimodal diffusion model that enhances polarized light and low-resolution EBSD data, enabling accurate inverse problem solutions with less data and noise robustness.

Contribution

It presents a novel unconditional multimodal diffusion approach trained on synthetic data that generalizes well to real, noisy, and low-resolution EBSD and PL data.

Findings

Model achieves near full-resolution performance with only 25% of EBSD data.

Significant improvements in grain boundary prediction, super-resolution, and denoising.

Model trained on synthetic data generalizes effectively to real-world data.

Abstract

In spite of the utility of 3-D electron back-scattered diffraction (EBSD) microscopy, the data collection process can be time-consuming with serial-sectioning. Hence, it is natural to look at other modalities, such as polarized light (PL) data, to accelerate EBSD data collection, supplemented with shared information. Complementarily, features in chaotic PL data could even be enriched with a handful of EBSD measurements. To inherently learn the complex dynamics between EBSD and PL to solve these inverse problems, we use an unconditional multimodal diffusion model, motivated by progress in diffusion models for inverse problems. Although trained solely on synthetic data once, our model has strong generalizable capabilities on real data which can be low-resolution, noisy, corrupted, and misregistered. With inference-time scaling, we show gains in performance on a variety of objectives including grain boundary prediction, super-resolution, and denoising. With our model, we demonstrate that there is little difference from full resolution performance with only 25% (1/4 the resolution) of EBSD data and corrupted PL data.