{
  "abstract": "Conventional low-pass filters attenuate or remove high-frequency Fourier amplitudes but leave the phases intact wherever the amplitude response stays above zero. As a result, fine-scale detail of the original signal can persist beyond the target resolution. A sharp cutoff removes this detail but produces ringing artifacts. To address both limitations, this work introduces ramped phase randomization, which extends a conventional low-pass filter by adding random offsets to Fourier phases that increase smoothly with spatial frequency. For cryo-EM maps, the correlation between a map at the target resolution and an ideal, noiseless map sets the levels of the phase offsets, with a correction for the finite resolution of the input map. On three published map pairs, ramped phase filtering reduced the higher resolution parent's fine detail to a level similar to that of independently determined maps at the lower target resolution, whereas amplitude-only filtering kept the parent's sharp features. The FSCs between the filtered maps and their parents closely resembled those of the experimental maps. The same filter also serves as a data augmentation approach for neural network-based machine learning. With ramped phase randomization, each augmented copy keeps the power spectrum and low-frequency content of the original example but has a different high-frequency pattern. For simulated CryoBench particles of 16 ribosome assembly states, phase augmentation raised mean test accuracy in all 20 combinations of training-set size and latent dimension, by 20.8 percentage points on average (range from 3.3 points to 44.2 points). On two general deep learning benchmarks, mean test accuracy rose from 92.15% to 96.99% with 60 labelled UCI Synthetic Control traces and from 53.44% to 74.27% with 500 labelled noisy MNIST images. The ramped phase randomization is implemented in an open-source Python package, with a companion browser application for interactive visualization of cryo-EM maps and filtering effects.",
  "abstract_shown": true,
  "affiliations": [
    "The Pennsylvania State University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "L. Y. Dang"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b79dcbadsz67h.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.30.763149",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.30.763149v1.full",
  "id": "b79dcbadsz67h",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.30.763149v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7225043368",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.30.763149v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b79dcbadsz67h.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:20:46Z",
      "native_id": "10.64898/2026.10.30.763149",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.30.763149v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "Low-pass filtering revisited: ramped phase randomization for cryo-EM map comparison and data augmentation",
  "title_text": "Low-pass filtering revisited: ramped phase randomization for cryo-EM map comparison and data augmentation",
  "type": "preprint",
  "updated_at": "2026-11-03T00:20:21Z",
  "url": "https://research.draconitic.com/paper/b79dcbadsz67h",
  "venue": "bioRxiv",
  "versions": []
}