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Volume 9
Issue 3
10.3390/photonics9030172
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Article

sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy

by
Bo Zhou
1,
Xiaoshuai Huang
2,
Junchao Fan
3,* and
Liangyi Chen
1,4,5,6
1
State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, School of Future Technology, Peking University, Beijing 100871, China
2
Biomedical Engineering Department, Peking University, Beijing 100871, China
3
Chongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4
PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
5
Beijing Academy of Artificial Intelligence, Beijing 100871, China
6
Shenzhen Bay Laboratory, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Photonics 2022, 9(3), 172; https://doi.org/10.3390/photonics9030172
Submission received: 23 February 2022 / Revised: 6 March 2022 / Accepted: 7 March 2022 / Published: 10 March 2022
(This article belongs to the Special Issue Biophotonics Imaging and Therapy: Advances, Applications, and Perspectives toward Translation and Clinics)
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Abstract

Structured illumination microscopy (SIM) is widely applied due to its high temporal and spatial resolution imaging ability. sCMOS cameras are often used in SIM due to their superior sensitivity, resolution, field of view, and frame rates. However, the unique single-pixel-dependent readout noise of sCMOS cameras may lead to SIM reconstruction artefacts and affect the accuracy of subsequent statistical analysis. We first established a nonuniform sCMOS noise model to address this issue, which incorporates the single-pixel-dependent offset, gain, and variance based on the SIM imaging process. The simulation indicates that the sCMOS pixel-dependent readout noise causes artefacts in the reconstructed SIM superresolution (SR) image. Thus, we propose a novel sCMOS noise-corrected SIM reconstruction algorithm derived from the imaging model, which can effectively suppress the sCMOS noise-related reconstruction artefacts and improve the signal-to-noise ratio (SNR).
Keywords: SIM; superresolution; sCMOS camera; noise correction SIM; superresolution; sCMOS camera; noise correction

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MDPI and ACS Style

Zhou, B.; Huang, X.; Fan, J.; Chen, L. sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy. Photonics 2022, 9, 172. https://doi.org/10.3390/photonics9030172

AMA Style

Zhou B, Huang X, Fan J, Chen L. sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy. Photonics. 2022; 9(3):172. https://doi.org/10.3390/photonics9030172

Chicago/Turabian Style

Zhou, Bo, Xiaoshuai Huang, Junchao Fan, and Liangyi Chen. 2022. "sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy" Photonics 9, no. 3: 172. https://doi.org/10.3390/photonics9030172

APA Style

Zhou, B., Huang, X., Fan, J., & Chen, L. (2022). sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy. Photonics, 9(3), 172. https://doi.org/10.3390/photonics9030172

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