Wang Weibo, Wu Biwei, Zhang Baoyuan, Li Xiaojun, Tan Jiubin
Opt Express. 2020 Aug 31;28(18):26028-26040. doi: 10.1364/OE.402109.
Radially polarized field under strong focusing has emerged as a powerful manner for fluorescence microscopy. However, the refractive index (RI) mismatch-induced aberrations seriously degrade imaging performance, especially under high numerical aperture (NA). Traditional adaptive optics (AO) method is limited by its tedious procedure. Here, we present a computational strategy that uses artificial neural networks to correct the aberrations induced by RI mismatch. There are no requirements for expensive hardware and complicated wavefront sensing in our framework when the deep network training is completed. The structural similarity index (SSIM) criteria and spatial frequency spectrum analysis demonstrate that our deep-learning-based method has a better performance compared to the widely used Richardson-Lucy (RL) deconvolution method at different imaging depth on simulation data. Additionally, the generalization of our trained network model is tested on new types of samples that are not present in the training procedure to further evaluate the utility of the network, and the performance is also superior to RL deconvolution.
强聚焦下的径向偏振场已成为荧光显微镜的一种强大方式。然而，折射率（RI）失配引起的像差严重降低了成像性能，尤其是在高数值孔径（NA）下。传统的自适应光学（AO）方法受其繁琐过程的限制。在此，我们提出一种计算策略，该策略使用人工神经网络来校正由RI失配引起的像差。在深度网络训练完成后，我们的框架对昂贵硬件和复杂波前传感没有要求。结构相似性指数（SSIM）标准和空间频谱分析表明，在模拟数据上的不同成像深度处，我们基于深度学习的方法与广泛使用的理查森 - Lucy（RL）反卷积方法相比具有更好的性能。此外，我们训练的网络模型的泛化能力在训练过程中不存在的新型样本上进行了测试，以进一步评估网络的效用，并且其性能也优于RL反卷积。
