Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy

Yiwei Hou; Wenyi Wang; Yunzhe Fu; Xichuan Ge; Meiqi Li; Peng Xi

doi:10.1186/s43593-024-00073-7

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Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy

eLight Vol. 4, (2024)

RESEARCH ARTICLE | Updated：2024-09-25

- Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy
- Affiliations：
  
  Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing 100871, China
  National Biomedical Imaging Center, Peking University, Beijing 100871, China
  Airy Technologies Co., Ltd., Beijing 100086, China
  School of Life Sciences, Peking University, Beijing 100871, China
- Author bio：
  
  *Meiqi Lilimeiqi@pku.edu.cn
  Peng Xixipeng@pku.edu.cn
- Funds：
- DOI：10.1186/s43593-024-00073-7
  CLC：
- Received：29 February 2024，
  
  Revised：13 June 2024，
  
  Accepted：2024-06-28，
  
  Published Online：06 August 2024，
  
  Published：2024-12
- Accepted：
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Yiwei Hou, Wenyi Wang, Yunzhe Fu, et al. Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy[J]. Elight, 2024, 4.
DOI：

Yiwei Hou, Wenyi Wang, Yunzhe Fu, et al. Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy[J]. Elight, 2024, 4. DOI： 10.1186/s43593-024-00073-7.

摘要

Abstract

Fluorescence microscopic imaging is essentially a convolution process distorted by random noise

limiting critical parameters such as imaging speed

duration

and resolution. Though algorithmic compensation has shown great potential to enhance these pivotal aspects

its fidelity remains questioned. Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity. Our approach employs a multi-resolution analysis (MRA) framework to extract the two main characteristics of fluorescence images against noise: across-edge contrast

and along-edge continuity. By constraining the two features in a model-solution framework using framelet and curvelet

we develop MRA deconvolution algorithms

which improve the signal-to-noise ratio (SNR) up to 10 dB higher than spatial derivative based penalties

and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference. We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity

enabling accomplishments of more challenging imaging tasks.

关键词

Keywords

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