Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver

Jintao Luo; Chuankang Li; Qiulan Liu; Junling Wu; Haifeng Li; Cuifang Kuang; Cuifang Kuang; Cuifang Kuang; Xiang Hao; Xu Liu; Xu Liu; Xu Liu

doi:10.3389/fphy.2020.00118

Frontiers in Physics (Apr 2020)

Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver

Jintao Luo,
Chuankang Li,
Qiulan Liu,
Junling Wu,
Haifeng Li,
Cuifang Kuang,
Cuifang Kuang,
Cuifang Kuang,
Xiang Hao,
Xu Liu,
Xu Liu,
Xu Liu

Affiliations

Jintao Luo: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Chuankang Li: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Qiulan Liu: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Junling Wu: Texas Instruments Semiconductor Technologies (Shanghai) Co., Ltd, Pudong, China
Haifeng Li: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Cuifang Kuang: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Cuifang Kuang: Ningbo Research Institute, Zhejiang University, Ningbo, China
Cuifang Kuang: Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China
Xiang Hao: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Xu Liu: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Xu Liu: Ningbo Research Institute, Zhejiang University, Ningbo, China
Xu Liu: Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China

DOI: https://doi.org/10.3389/fphy.2020.00118
Journal volume & issue: Vol. 8

Abstract

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Super-resolution microscopy enables images to be obtained at a resolution higher than that imposed by the diffraction limit of light. Structured illumination microscopy (SIM) is among the fastest super-resolution microscopy techniques currently in use, and it has gained popularity in the field of cytobiology research owing to its low photo-toxicity and widefield modality. In typical SIM, a fluorescent sample is excited by sinusoidal patterns by employing a linear strategy to reconstruct super-resolution images. However, this strategy fails in cases where non-sinusoidal illumination patterns are used. In this study, we propose the least-squares SIM (LSQ-SIM) approach, which is an efficient super-resolution reconstruction algorithm in the framework of least-squares regression that can process raw SIM data under both sinusoidal and non-sinusoidal illuminations. The results obtained in this study indicate the potential of LSQ-SIM for use in structured illumination microscopy and its various application fields.

Published in Frontiers in Physics

ISSN: 2296-424X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Science: Physics
Website: https://www.frontiersin.org/journals/physics

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