{
  "annotation_id": "65a47f72-2e4e-46db-baf4-8215f917d93a",
  "date_created": "2026-02-17T05:53:08.248000Z",
  "date_modified": "2026-02-17T05:53:08.248000Z",
  "file_hash": "872fd0933ff2462455f34af43f5d9cd166da3b09a1553b0c25294c0bb86fe196",
  "private": false,
  "record": {
    "abstract": "Ultrathin multimode fibers (MMFs) promise endoscopes with hair-scale diameters for accessing sub-millimeter anatomy, but in MMF far-field imaging the required small collection aperture drives speckle-dominated measurements that rapidly degrade image fidelity. Here we present Speckle Clean Network (SCNet), a physics-guided foundation model for universal speckle removal that makes photon-limited, single-fiber collection compatible with high-fidelity reconstruction across diverse scattering conditions without target-specific retraining. SCNet combines a Mixture of Experts (MoE) architecture with material-aware routing, wavelet-based frequency decomposition to separate structure from speckle across sub-bands, and a curriculum-style optimization that progressively enforces spectral consistency before spatial fidelity. Using an ultrathin dual-fiber holographic probe, we deliver wavefront-shaped illumination through one s and collect backscattered photons through a parallel MMF. We validate SCNet on 3D plastic objects over varying working distances, resolve 5.66 lp/mm on a paper USAF target, and restore fine structures on leaves and metal surfaces. On rabbit heart and kidney tissues, SCNet improves recovery of low-contrast anatomical texture under the same ultrathin collection constraint. We further compress SCNet through multi-teacher distillation to reduce computation while preserving reconstruction quality, enabling inference at 60 FPS. This work effectively decouples image quality from probe size, establishing a speckle-free ultrathin endoscopy for stand-off imaging in confined spaces.",
    "arxiv_id": "2601.06448",
    "authors": [
      "Xianrui Zeng",
      "Yirui Zang",
      "Pengfei Liu",
      "Fei Yu",
      "Yang Yang",
      "Tom\u00e1\u0161 \u010ci\u017em\u00e1r",
      "Yang Du"
    ],
    "categories": [
      "physics.optics"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Physics-guided foundation model for universal speckle removal in ultrathin multimode fiber imaging",
    "url": "https://arxiv.org/abs/2601.06448",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "787e082f-3fc5-4407-82cd-226ff3574080",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}