Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration.
Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration.
复制标题
基于像差先验知识的深度学习荧光显微镜图像增强。
DOI：
10.1364/ol.418997
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发表时间：
2021-05
期刊：
影响因子：
3.6
通讯作者：
Lejia Hu;Shuwen Hu;W. Gong;Ke Si
中科院分区：
文献类型：
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作者：
Lejia Hu;Shuwen Hu;W. Gong;Ke Si
在这封信中，我们提出了一种具有潜在畸变先验知识的深度学习方法，以增强荧光显微镜，而无需额外的硬件。该方法能有效地降低噪声，提高采集图像的峰值信噪比，且速度快。在三种商用荧光显微镜上验证了该方法的增强性能和推广性。这项工作提供了一个计算的替代，以克服由生物样品引起的降解，它有可能进一步应用于生物应用。
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.