{
  "abstract": "Computational hyperspectral imaging reconstructs spatial-spectral information from limited optical measurements. Linear reconstructors are efficient and interpretable, but often underperform deep nonlinear models, which typically require substantial training data and computational resources. Here we present a theory-guided encoding-reconstruction co-design framework that achieves, to our knowledge, state-of-the-art linear reconstruction performance. We establish a theory for the exact five-term decomposition of reconstruction risk and introduce a method for diagnosing the statistical-coverage lower bound within a fixed principal component analysis envelope, which together inform the optimized design of hyperspectral imaging systems. The framework applies across coded-aperture snapshot spectral imaging (CASSI), diffractive optical element imaging and spectral filter array imaging, achieving 34.76 dB mean PSNR in single-shot CASSI, outperforming a deep baseline by 9.43 dB with only five source scenes, and matching empirical cross-domain risk within 0.2%. Our results establish a data-efficient and theoretically grounded route to high-performance linear hyperspectral reconstruction, while providing a quantitative basis for identifying whether reconstruction is limited by optical encoding, reconstruction design or statistical coverage.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.34935",
  "author_total": 7,
  "authors": [
    {
      "name": "Shenzhan Hong"
    },
    {
      "name": "Shuxing Yang"
    },
    {
      "name": "Taojun Ying"
    },
    {
      "name": "Fujia Chen"
    },
    {
      "name": "Zongyin Yang"
    },
    {
      "name": "Hongsheng Chen"
    },
    {
      "name": "Yihao Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/97v7x9h88j4g4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34935",
  "fulltext_url": null,
  "id": "97v7x9h88j4g4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34935",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.optics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.34935",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/97v7x9h88j4g4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.34935",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34935"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Data-efficient linear hyperspectral imaging through risk-guided co-design",
  "title_text": "Data-efficient linear hyperspectral imaging through risk-guided co-design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/97v7x9h88j4g4",
  "venue": "arXiv",
  "versions": []
}