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

Hu Lejia; Hu Shuwen; Gong Wei; Si Ke

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

机译：基于深度学习的荧光显微镜与畸变知识的图像增强

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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. (C) 2021 Optical Society of America

机译：在这封信中，我们提出了一种深度学习方法，利用潜在像差的先验知识，在不增加硬件的情况下增强荧光显微镜。该方法能有效地降低噪声，提高高速采集图像的峰值信噪比。在三台商用荧光显微镜上证明了该方法的增强性能和通用性。这项工作为克服生物样品引起的降解提供了一种计算方法，并有可能在生物应用中得到进一步应用。（2021）美国光学学会

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《Optics Letters》 |2021年第9期|共4页
作者
Hu Lejia; Hu Shuwen; Gong Wei; Si Ke;
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作者单位

Zhejiang Univ State Key Lab Modern Opt Instrumentat Sch Med Dept Neurol Affiliated Hosp 1 Hangzhou 310003 Peoples R China;

Zhejiang Univ Coll Opt Sci &

Engn Hangzhou 310027 Peoples R China;

Zhejiang Univ State Key Lab Modern Opt Instrumentat Sch Med Dept Neurol Affiliated Hosp 1 Hangzhou 310003 Peoples R China;

Zhejiang Univ State Key Lab Modern Opt Instrumentat Sch Med Dept Neurol Affiliated Hosp 1 Hangzhou 310003 Peoples R China;

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原文格式 PDF
正文语种 eng
中图分类计量学;光学;
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2. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning [J] . Yair Rivenson, Tairan Liu, Zhensong Wei, 光：科学与应用（英文版） . 2019,第001期
3. Imaging Enhancement of Light-Sheet Fluorescence Microscopy via Deep Learning [J] . Bai Chen, Liu Chao, Yu Xianghua, IEEE Photonics Technology Letters . 2019,第22期

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5. Radiotherapy-Specific Imaging Prior Knowledge and Its Impact in Deep Learning Based Cranial Pseudo-CT Generation [J] . Law M., Yuan J., Lo G., Medical Physics . 2019,第6期

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6. Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning [C] . Roman Spilger, Thomas Wollmann, Yu Qiang, International conference on medical imaging computing and computer-assisted intervention;International workshop on deep learning in medical image analysis;International workshop on multimodal learning for clinical decision support . 2018

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7. On-Board Image Augmentation Using Prior Image and Deep Learning for Image-Guided Radiation Therapy [D] . Chen, Yingxuan. 2019

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9. Ultra-Deep Imaging with Cellular Resolution: Enhanced Two-Photon Fluorescence Microscopy with the Use of a Wide Area Photodetector [O] . Crosignani Viera, Dvornikov Alexander, Gratton Enrico 2012

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10. Enhancing Incremental Learning Processes With Knowledge-Based Systems. [R] . Fischer, G., Lemke, A., Nieper-Lemke, H. 1988

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

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