Opt Lett. 2021 May 1;46(9):2055-2058. doi: 10.1364/OL.418997.
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.
在这封信件中，我们提出了一种利用潜在偏差先验知识的深度学习方法，无需额外的硬件即可增强荧光显微镜。该方法可以有效地降低噪声，并在高速下提高采集图像的峰值信噪比。该方法在三种商用荧光显微镜上的增强性能和泛化能力得到了验证。这项工作为克服生物样本引起的退化提供了一种计算替代方法，并且有可能进一步应用于生物应用中。
