{
  "annotation_id": "4da56d1c-3fcc-4ef1-8a42-0d4cb35cfbd7",
  "date_created": "2026-02-17T05:53:12.527000Z",
  "date_modified": "2026-02-17T05:53:12.527000Z",
  "file_hash": "69707c805e8d130b05934c07657ea565c813e69cbe8197632d6d733dd2b5e12c",
  "private": false,
  "record": {
    "abstract": "The Noise2Void technique is demonstrated for successful denoising of atomic-resolution scanning transmission electron microscopy (STEM) images. The technique is applied to denoising atomic resolution images and videos of gold adatoms on a graphene surface within a graphene liquid cell, with the denoised experimental data qualitatively demonstrating improved visibility of both the Au adatoms and the graphene lattice. The denoising performance is quantified by comparison to similar simulated data and the approach is found to significantly outperform both total variation and simple Gaussian blurring. Compared to other denoising methods, the Noise2Void technique has the combined advantages that it requires no manual intervention during training or denoising, no prior knowledge of the sample and is compatible with real time data acquisition rates of at least 45 frames per second.",
    "arxiv_id": "2601.07699",
    "authors": [
      "William Thornley",
      "Sam Sullivan-Allsop",
      "Rongsheng Cai",
      "Nick Clark",
      "Roman Gorbachev",
      "Sarah J. Haigh"
    ],
    "categories": [
      "cond-mat.mtrl-sci",
      "physics.ins-det"
    ],
    "doi": "10.1038/s41524-025-01939-1",
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "Noise2Void for Denoising Atomic Resolution Scanning Transmission Electron Microscopy Images",
    "url": "https://arxiv.org/abs/2601.07699",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "024056d5-021f-47ea-949c-0685d008c44f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}