Frontiers in Physics | |
Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver | |
Cuifang Kuang1  Xu Liu1  Xiang Hao3  Chuankang Li3  Haifeng Li3  Jintao Luo3  Qiulan Liu3  Junling Wu4  | |
[1] Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China;Ningbo Research Institute, Zhejiang University, Ningbo, China;State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China;Texas Instruments Semiconductor Technologies (Shanghai) Co., Ltd, Pudong, China; | |
关键词: super-resolution imaging; structured illumination microscopy; reconstruction algorithm; optimization; least squares; | |
DOI : 10.3389/fphy.2020.00118 | |
来源: DOAJ |
【 摘 要 】
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.
【 授权许可】
Unknown