{
  "abstract": "Lens-free on-chip microscopy (LFOM) enables compact, wide-field imaging, yet reconstructing a complex field from a single in-line hologram involves two coupled challenges: phase must be inferred from an intensity-only measurement, while fringe-scale information must be recovered within a finite numerical-aperture bandwidth. We introduce XPAR-Net, which addresses these challenges through coordinated learning across complementary representation paradigms. Field-wide channel-relation modeling captures selective dependencies among holographic responses, whereas localized spatial refinement preserves fine structural variations. Cross-Domain Alignment and Fusion reconciles these representations and confines learnable spectral residual correction to the modeled acquisition support. Training further couples reference-domain complex-field fidelity with in-support spectral-profile, local-structural, and input-relative refinement constraints. On the complete 11-category in-distribution test set, XPAR-Net improved average PSNR by 1.28 dB and SSIM by $$2.9\\%$$ over HoloFormer, with only $$8.8\\%$$ of HoloFormer’s trainable parameters and $$19.0\\%$$ of its MACs for a $$256\\times 256$$ input patch. These results highlight coordinated cross-paradigm modeling as an effective route to accurate and computationally compact single-frame LFOM reconstruction under the evaluated conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Yanbo Zhao"
    },
    {
      "name": "Xiongfei Su"
    },
    {
      "name": "Ning Xu"
    },
    {
      "name": "Yachen Gao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bfnvx90qt6atq.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73646-w",
  "fulltext_url": null,
  "id": "bfnvx90qt6atq",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73646-w",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3107|Digital Holography and Microscopy"
    ]
  ],
  "openalex": "W7223152602",
  "pdf_url": "https://www.nature.com/articles/s41598-026-73646-w_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/bfnvx90qt6atq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "10.1038/s41598-026-73646-w",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73646-w"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Digital Holography and Microscopy",
      "subfield": "amo"
    }
  ],
  "title": "XPAR-Net: cross-paradigm alignment and reconstruction network for high-fidelity lens-free on-chip microscopy",
  "title_text": "XPAR-Net: cross-paradigm alignment and reconstruction network for high-fidelity lens-free on-chip microscopy",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/bfnvx90qt6atq",
  "venue": "Scientific Reports",
  "versions": []
}