MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy
Recent developments in single-molecule localization microscopy (SMLM) enable researchers to study macromolecular structures at the nanometer scale. However, due to the complexity of imaging process, there are a variety of complex heterogeneous noises in SMLM data. The conventional denoising methods...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.1083558/full |
| _version_ | 1797987405441007616 |
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| author | Qianghui Feng Qihang Song Meng Yan Zhen Li Huang Zhengxia Wang |
| author_facet | Qianghui Feng Qihang Song Meng Yan Zhen Li Huang Zhengxia Wang |
| author_sort | Qianghui Feng |
| collection | DOAJ |
| description | Recent developments in single-molecule localization microscopy (SMLM) enable researchers to study macromolecular structures at the nanometer scale. However, due to the complexity of imaging process, there are a variety of complex heterogeneous noises in SMLM data. The conventional denoising methods in SMLM can only remove a single type of noise. And, most of these denoising algorithms require manual parameter setting, which is difficult and unfriendly for biological researchers. To solve these problems, we propose a multi-step adaptive denoising framework called MSDenoiser, which incorporates multiple noise reduction algorithms and can gradually remove heterogeneous mixed noises in SMLM. In addition, this framework can adaptively learn algorithm parameters based on the localization data without manually intervention. We demonstrate the effectiveness of the proposed denoising framework on both simulated data and experimental data with different types of structures (microtubules, nuclear pore complexes and mitochondria). Experimental results show that the proposed method has better denoising effect and universality. |
| first_indexed | 2024-04-11T07:48:08Z |
| format | Article |
| id | doaj.art-3d90b4837e914c749df4d6291faa3f26 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-11T07:48:08Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-3d90b4837e914c749df4d6291faa3f262022-12-22T04:36:11ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-12-011010.3389/fphy.2022.10835581083558MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopyQianghui Feng0Qihang Song1Meng Yan2Zhen Li Huang3Zhengxia Wang4School of Computer Science and Technology, Hainan University, Haikou, ChinaKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, ChinaSchool of Computer Science and Technology, Hainan University, Haikou, ChinaKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, ChinaSchool of Computer Science and Technology, Hainan University, Haikou, ChinaRecent developments in single-molecule localization microscopy (SMLM) enable researchers to study macromolecular structures at the nanometer scale. However, due to the complexity of imaging process, there are a variety of complex heterogeneous noises in SMLM data. The conventional denoising methods in SMLM can only remove a single type of noise. And, most of these denoising algorithms require manual parameter setting, which is difficult and unfriendly for biological researchers. To solve these problems, we propose a multi-step adaptive denoising framework called MSDenoiser, which incorporates multiple noise reduction algorithms and can gradually remove heterogeneous mixed noises in SMLM. In addition, this framework can adaptively learn algorithm parameters based on the localization data without manually intervention. We demonstrate the effectiveness of the proposed denoising framework on both simulated data and experimental data with different types of structures (microtubules, nuclear pore complexes and mitochondria). Experimental results show that the proposed method has better denoising effect and universality.https://www.frontiersin.org/articles/10.3389/fphy.2022.1083558/fullnoise reductionsuper-resolution image processingmulti-step denoising frameworkadaptive parameter selectionlocalization data |
| spellingShingle | Qianghui Feng Qihang Song Meng Yan Zhen Li Huang Zhengxia Wang MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy Frontiers in Physics noise reduction super-resolution image processing multi-step denoising framework adaptive parameter selection localization data |
| title | MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy |
| title_full | MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy |
| title_fullStr | MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy |
| title_full_unstemmed | MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy |
| title_short | MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy |
| title_sort | msdenoiser muti step adaptive denoising framework for super resolution image from single molecule localization microscopy |
| topic | noise reduction super-resolution image processing multi-step denoising framework adaptive parameter selection localization data |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.1083558/full |
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