{
  "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_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.10898",
  "author_total": 6,
  "authors": [
    {
      "name": "Umang Garg"
    },
    {
      "name": "Warren Zamudio"
    },
    {
      "name": "McLean P. Echlin"
    },
    {
      "name": "Samantha H. Daly"
    },
    {
      "name": "Tresa M. Pollock"
    },
    {
      "name": "B. S. Manjunath"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4b7amkxw1s6m6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.10898",
  "fulltext_url": null,
  "id": "4b7amkxw1s6m6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.10898",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "openalex",
      "1707|Advanced Image Processing Techniques"
    ]
  ],
  "openalex": "W7212254024",
  "pdf_url": "https://arxiv.org/pdf/2609.10898",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4b7amkxw1s6m6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:26Z",
      "native_id": "2609.10898",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.10898"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Symmetry-aware super-resolution of crystal orientation maps via invariant latent-space learning",
  "title_text": "Symmetry-aware super-resolution of crystal orientation maps via invariant latent-space learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:26Z",
  "url": "https://research.draconitic.com/paper/4b7amkxw1s6m6",
  "venue": "arXiv",
  "versions": []
}