A lightweight deep learning framework for fast, real-time super-resolution fluctuation imaging.
- DOI
- 10.1016/j.bpr.2026.100279
- Published
- 2026 Sep 9
- Container
- Biophysical reports
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1016/j.bpr.2026.100279,
title = {A lightweight deep learning framework for fast, real-time super-resolution fluctuation imaging.},
author = {Tekpınar M and Komen J and Valenta H and Huo R and de Zwaan K and Dedecker P and Tomen N and Grußmayer K},
year = {2026},
journal = {Biophysical reports},
doi = {10.1016/j.bpr.2026.100279},
url = {https://doi.org/10.1016/j.bpr.2026.100279}
}RIS
TY - JOUR TI - A lightweight deep learning framework for fast, real-time super-resolution fluctuation imaging. AU - Tekpınar M AU - Komen J AU - Valenta H AU - Huo R AU - de Zwaan K AU - Dedecker P AU - Tomen N AU - Grußmayer K PY - 2026 JO - Biophysical reports DO - 10.1016/j.bpr.2026.100279 UR - https://doi.org/10.1016/j.bpr.2026.100279 ER -
APA
M, T., J, K., H, V., R, H., K, D. Z., P, D., N, T., & K, G. (2026). A lightweight deep learning framework for fast, real-time super-resolution fluctuation imaging.. Biophysical reports. https://doi.org/10.1016/j.bpr.2026.100279
Source records
- pubmed · retrieved 2026-09-25T13:32:50.632Z