Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver
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 cy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphy.2020.00118/full |
| _version_ | 1818252273873059840 |
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| author | Jintao Luo Chuankang Li Qiulan Liu Junling Wu Haifeng Li Cuifang Kuang Cuifang Kuang Cuifang Kuang Xiang Hao Xu Liu Xu Liu Xu Liu |
| author_facet | Jintao Luo Chuankang Li Qiulan Liu Junling Wu Haifeng Li Cuifang Kuang Cuifang Kuang Cuifang Kuang Xiang Hao Xu Liu Xu Liu Xu Liu |
| author_sort | Jintao Luo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T16:21:34Z |
| format | Article |
| id | doaj.art-b458fbf49fbe4cc6a0cedc36f16a4e8b |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-12-12T16:21:34Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-b458fbf49fbe4cc6a0cedc36f16a4e8b2022-12-22T00:18:59ZengFrontiers Media S.A.Frontiers in Physics2296-424X2020-04-01810.3389/fphy.2020.00118534453Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares SolverJintao Luo0Chuankang Li1Qiulan Liu2Junling Wu3Haifeng Li4Cuifang Kuang5Cuifang Kuang6Cuifang Kuang7Xiang Hao8Xu Liu9Xu Liu10Xu Liu11State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaTexas Instruments Semiconductor Technologies (Shanghai) Co., Ltd, Pudong, ChinaState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaNingbo Research Institute, Zhejiang University, Ningbo, ChinaCollaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, ChinaState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, ChinaNingbo Research Institute, Zhejiang University, Ningbo, ChinaCollaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, ChinaSuper-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.https://www.frontiersin.org/article/10.3389/fphy.2020.00118/fullsuper-resolution imagingstructured illumination microscopyreconstruction algorithmoptimizationleast squares |
| spellingShingle | Jintao Luo Chuankang Li Qiulan Liu Junling Wu Haifeng Li Cuifang Kuang Cuifang Kuang Cuifang Kuang Xiang Hao Xu Liu Xu Liu Xu Liu Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver Frontiers in Physics super-resolution imaging structured illumination microscopy reconstruction algorithm optimization least squares |
| title | Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver |
| title_full | Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver |
| title_fullStr | Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver |
| title_full_unstemmed | Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver |
| title_short | Super-Resolution Structured Illumination Microscopy Reconstruction Using a Least-Squares Solver |
| title_sort | super resolution structured illumination microscopy reconstruction using a least squares solver |
| topic | super-resolution imaging structured illumination microscopy reconstruction algorithm optimization least squares |
| url | https://www.frontiersin.org/article/10.3389/fphy.2020.00118/full |
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