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This research presents SG-SRAN, a specialized machine learning architecture designed to enhance the spatial resolution of crystal-orientation maps derived from EBSD data. By operating in a latent space that inherently respects crystal symmetry groups, the model prevents the blurring of grain boundaries common in standard Euclidean interpolation methods. The approach achieves performance comparable to massive backbones with significantly fewer trainable parameters, offering an efficient solution for materials science applications requiring high-fidelity microstructural analysis.
Symmetry-aware super-resolution of crystal orientation maps via invariant latent-space learning
arXiv:2609.10898v1 Announce Type: cross Abstract: Crystal-orientation maps are physical fields defined only up to crystal symmetry; electron backscatter diffraction (EBSD) resolves them experimentally, but acquisition-time constraints limit spatial resolution. Unlike conventional images, EBSD data lie on the quotient space $\mathrm{SO}(3)/G$, where $G$ is the crystal-symmetry group. Standard Euclidean interpolation can therefore mix symmetry-equivalent representations and blur grain boundaries. We introduce the Symmetry-Group-Aware Super-Resolution Attention Network (SG-SRAN), which incorporates crystal symmetry and boundary preservation by design. A frozen, locally isometric encoder maps equivalent orientations to a common latent representation in which Euclidean distance approximates misorientation. Super-resolution is performed in this space, with each high-resolution token restricted to a feature-consistent local support to prevent cross-boundary mixing. A dictionary-based decoder then recovers valid orientations. Across FCC and HCP benchmarks, SG-SRAN matches 15-16 million parameter backbones using only 27-49k trainable parameters, while achieving the lowest p68 errors, highest inverse-pole-figure fidelity, and zero-shot transfer to unseen alloys.
Abstract:Crystal-orientation maps are physical fields defined only up to crystal symmetry; electron backscatter diffraction (EBSD) resolves them experimentally, but acquisition-time constraints limit spatial resolution. Unlike conventional images, EBSD data lie on the quotient space $\mathrm{SO}(3)/G$, where $G$ is the crystal-symmetry group. Standard Euclidean interpolation can therefore mix symmetry-equivalent representations and blur grain boundaries. We introduce the Symmetry-Group-Aware Super-Resolution Attention Network (SG-SRAN), which incorporates crystal symmetry and boundary preservation by design. A frozen, locally isometric encoder maps equivalent orientations to a common latent representation in which Euclidean distance approximates misorientation. Super-resolution is performed in this space, with each high-resolution token restricted to a feature-consistent local support to prevent cross-boundary mixing. A dictionary-based decoder then recovers valid orientations. Across FCC and HCP benchmarks, SG-SRAN matches 15-16 million parameter backbones using only 27-49k trainable parameters, while achieving the lowest p68 errors, highest inverse-pole-figure fidelity, and zero-shot transfer to unseen alloys.
Computer Vision and Pattern Recognition (cs.CV); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
arXiv:2609.10898 [cs.CV]
(or arXiv:2609.10898v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.10898 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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