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Journal article

Structured illumination microscopy with noise-controlled image reconstructions

Abstract:
Super-resolution structured illumination microscopy (SIM) has become a widely used method for biological imaging. Standard reconstruction algorithms, however, are prone to generate noise-specific artifacts that limit their applicability for lower signal-to-noise data. Here we present a physically realistic noise model that explains the structured noise artifact, which we then use to motivate new complementary reconstruction approaches. True-Wiener-filtered SIM optimizes contrast given the available signal-to-noise ratio, and flat-noise SIM fully overcomes the structured noise artifact while maintaining resolving power. Both methods eliminate ad hoc user-adjustable reconstruction parameters in favor of physical parameters, enhancing objectivity. The new reconstructions point to a trade-off between contrast and a natural noise appearance. This trade-off can be partly overcome by further notch filtering but at the expense of a decrease in signal-to-noise ratio. The benefits of the proposed approaches are demonstrated on focal adhesion and tubulin samples in two and three dimensions, and on nanofabricated fluorescent test patterns.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41592-021-01167-7

Authors

More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Physiology Anatomy and Genetics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Role:
Author
ORCID:
0000-0002-1612-9699
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Physiology Anatomy and Genetics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Physiology Anatomy and Genetics
Role:
Author


More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
107457/Z/15/Z
More from this funder
Funder identifier:
https://ror.org/00k4n6c32
Grant:
766181


Publisher:
Springer Nature
Journal:
Nature Methods More from this journal
Volume:
18
Issue:
7
Pages:
821-828
Publication date:
2021-06-14
Acceptance date:
2021-04-26
DOI:
EISSN:
1548-7105
ISSN:
1548-7091
Pmid:
34127855


Language:
English
Keywords:
Pubs id:
1182787
UUID:
uuid_b6f72d9a-6deb-4b28-83be-e02e68b12a2f
Local pid:
pubs:1182787
Source identifiers:
W3172179053
Deposit date:
2025-12-05
ARK identifier:

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