Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation Article Swipe
Weisong Zhao
,
Xiaoshuai Huang
,
Jianyu Yang
,
Guo‐Hua Qiu
,
Liying Qu
,
Yue Zhao
,
Shiqun Zhao
,
Ziying Luo
,
Xinwei Wang
,
Yaming Jiu
,
Heng Mao
,
Xumin Ding
,
Jiubin Tan
,
Ying Hu
,
Leiting Pan
,
Liangyi Chen
,
Haoyu Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.12.01.518675
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.12.01.518675
In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions, which is especially intractable in emerging deep-learning ones. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) framework to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Beyond that, we provide a strategy for learning-based restorations, allowing a direct detection of both data and model uncertainties, and expect the representative cases can inspire further advances in this rapidly developing field.
Related Topics
Concepts
Computer science
Reliability (semiconductor)
Resolution (logic)
Fourier transform
Scale (ratio)
Artificial intelligence
Noise (video)
Superresolution
Quality (philosophy)
Pattern recognition (psychology)
Computer vision
Algorithm
Image (mathematics)
Mathematics
Physics
Geography
Cartography
Power (physics)
Mathematical analysis
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.12.01.518675
- https://www.biorxiv.org/content/biorxiv/early/2022/12/02/2022.12.01.518675.full.pdf
- OA Status
- green
- Cited By
- 9
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311179794
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311179794Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.12.01.518675Digital Object Identifier
- Title
-
Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-02Full publication date if available
- Authors
-
Weisong Zhao, Xiaoshuai Huang, Jianyu Yang, Guo‐Hua Qiu, Liying Qu, Yue Zhao, Shiqun Zhao, Ziying Luo, Xinwei Wang, Yaming Jiu, Heng Mao, Xumin Ding, Jiubin Tan, Ying Hu, Leiting Pan, Liangyi Chen, Haoyu LiList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.12.01.518675Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/12/02/2022.12.01.518675.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/12/02/2022.12.01.518675.full.pdfDirect OA link when available
- Concepts
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Computer science, Reliability (semiconductor), Resolution (logic), Fourier transform, Scale (ratio), Artificial intelligence, Noise (video), Superresolution, Quality (philosophy), Pattern recognition (psychology), Computer vision, Algorithm, Image (mathematics), Mathematics, Physics, Geography, Cartography, Power (physics), Mathematical analysis, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-12-02 |
| publication_year | 2022 |
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