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Cited by 363

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Parameter-free image resolution estimation based on decorrelation analysis

Open access
Descloux, A., Grußmayer, K. S., Radenovic, A.·2019·Nature Methods

Abstract

Super-resolution microscopy opened diverse new avenues of research by overcoming the resolution limit imposed by diffraction. Exploitation of the fluorescent emission of individual fluorophores made it possible to reveal structures beyond the diffraction limit. To accurately determine the resolution achieved during imaging is challenging with existing metrics. Here, we propose a method for assessing the resolution of individual super-resolved images based on image partial phase autocorrelation. The algorithm is model-free and does not require any user-defined parameters. We demonstrate its performance on a wide variety of imaging modalities, including diffraction-limited techniques. Finally, we show how our method can be used to optimize image acquisition and post-processing in super-resolution microscopy. Decorrelation analysis offers an improved method for assessing image resolution that works on a single image and is insensitive to common image artifacts. The method can be applied generally to any type of microscopy images.

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Metadata

Authors
Descloux, A., Grußmayer, K. S., & Radenovic, A.
Venue
Nature Methods
Published
2019-08-26
Volume
16
Issue
9
Pages
918-924
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W2969928967
Cited by
363
References
40
FWCI
12.03
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Parameter-free image resolution estimation based on decorrelation analysis. https://app.cochat.ai/papers/10.1038/s41592-019-0515-7
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Descloux, A., Grußmayer, K. S., & Radenovic, A. (2019). Parameter-free image resolution estimation based on decorrelation analysis. Nature Methods. https://doi.org/10.1038/s41592-019-0515-7

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.