Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration

In this Letter, we propose a deep learning method with prior knowledge of potential aberration to enhance the fluorescence microscopy without additional hardware. The proposed method could effectively reduce noise and improve the peak signal-to-noise ratio of the acquired images at high speed. The e...

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Bibliographic Details
Published in:Optics letters 2021-05, Vol.46 (9), p.2055-2058
Main Authors: Hu, Lejia, Hu, Shuwen, Gong, Wei, Si, Ke
Format: Article
Language:English
Subjects:
Aberration
Deep learning
Fluorescence
Image acquisition
Image enhancement
Microscopes
Microscopy
Noise reduction
Signal to noise ratio
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Summary:In this Letter, we propose a deep learning method with prior knowledge of potential aberration to enhance the fluorescence microscopy without additional hardware. The proposed method could effectively reduce noise and improve the peak signal-to-noise ratio of the acquired images at high speed. The enhancement performance and generalization of this method is demonstrated on three commercial fluorescence microscopes. This work provides a computational alternative to overcome the degradation induced by the biological specimen, and it has the potential to be further applied in biological applications.
ISSN:0146-9592
1539-4794
DOI:10.1364/OL.418997