Structured illumination microscopy (SIM), with the advantages of full-field imaging and low photo-damage, is one of the most well-established fluorescence super-resolution microscopy techniques that raised great interest in biological sciences. However, conventional SIM techniques generally require at least nine images for image reconstruction, and the quality of super-resolution significantly depends on high-accuracy illumination parameter estimation, which is usually computationally intense and time-consuming. To address these issues, we propose a robust seven-frame SIM reconstruction algorithm with accelerated correlation-enabled parameter estimation. First, a modulation-assigned spatial filter is employed to remove unreliable backgrounds associated with low signal-to-noise ratios. Then, we propose a coarse-to-fine accelerated correlation algorithm to eliminate the redundant iterations of the traditional correlation-based scheme. The frame reduction is achieved by a specially designed phase-shifting strategy combined with pixel-wise fluorescence pre-calibration. We experimentally demonstrate that, compared with conventional iterative correlation-based methods, the proposed algorithm improves the computational efficiency by a factor of 4.5 while maintaining high accuracy illumination parameter estimation. Meanwhile, our method achieves high-quality super-resolution reconstruction even with a reduction in two raw images, which improves the efficiency of image acquisition and ensures the robustness toward complex experimental environments.
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Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation
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10 October 2022
Research Article|
October 10 2022
Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation

Special Collection:
Advances in Optical Microscopy for Bioimaging
Jiaming Qian
;
Jiaming Qian
a)
(Conceptualization, Data curation, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210094, China
2
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210019, China
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
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Yu Cao;
Yu Cao
(Data curation, Validation, Writing – original draft)
1
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210094, China
2
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210019, China
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
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Kailong Xu
;
Kailong Xu
(Software, Validation)
1
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210094, China
2
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210019, China
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
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Ying Bi;
Ying Bi
(Validation, Writing – original draft)
1
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210094, China
2
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210019, China
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
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Weiyi Xia;
Weiyi Xia
(Visualization, Writing – original draft)
1
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210094, China
2
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210019, China
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
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Qian Chen
;
Qian Chen
(Conceptualization, Funding acquisition, Writing – review & editing)
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
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Chao Zuo
Chao Zuo
b)
(Conceptualization, Funding acquisition, Supervision, Writing – review & editing)
1
Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210094, China
2
Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology
, Nanjing, Jiangsu Province 210019, China
3
Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense
, Nanjing, Jiangsu Province 210094, China
b)Author to whom correspondence should be addressed: zuochao@njust.edu.cn
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a)
Electronic mail: jiaming_qian@njust.edu.cn
b)Author to whom correspondence should be addressed: zuochao@njust.edu.cn
Note: This paper is part of the APL Special Collection on Advances in Optical Microscopy for Bioimaging.
Appl. Phys. Lett. 121, 153701 (2022)
Article history
Received:
July 05 2022
Accepted:
September 10 2022
Citation
Jiaming Qian, Yu Cao, Kailong Xu, Ying Bi, Weiyi Xia, Qian Chen, Chao Zuo; Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation. Appl. Phys. Lett. 10 October 2022; 121 (15): 153701. https://doi.org/10.1063/5.0107510
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