Machine learning based adaptive optics for doughnut-shaped beam
Machine learning based adaptive optics for doughnut-shaped beam
复制标题
基于机器学习的环形光束自适应光学器件
DOI：
10.1364/oe.27.016871
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发表时间：
2019-06-10
期刊：
影响因子：
3.8
通讯作者：
Gong, Wei
中科院分区：
文献类型：
--
作者：
Zhang, Yiye;Wu, Chenxue;Gong, Wei
环形光束已广泛应用于超分辨率显微成像、微纳米结构光刻、超高密度存储和激光捕获等领域。然而，由于波前像差，如何在散射介质内保持环形焦点成为一个挑战。在这里，我们演示了一种基于机器学习的自适应光学方法，可以高速恢复环形焦点。在我们的方法中，建立了扭曲的环形强度点扩散函数与用于相位校正的前15个Zernikc模式的系数之间的关系。实验结果表明，即使使用个人计算机，也可以在大约 17 ms 内预测 101,784 个光学控制元件的波前像差。 200次重复测试，校正精度可达97.5%。此外，我们在理论上成功地将这种方法应用于扫描显微镜。凭借大量的光学控制元件和快速的运行速度，我们的方法可能为生物成像中的许多重要应用铺平道路，例如深层组织受激发射损耗（STED）显微镜。 (C) 2019 年美国光学学会根据 OSA 开放获取出版协议条款
The doughnut-shaped beam has been widely applied in the field of super-resolution microscopic imaging, micro-nanostructure lithography, ultra-high-density storage, and laser trapping. However, how to maintain the doughnut-shaped focus inside the scattering medium becomes a challenge, due to the wavefront aberrations. Here we demonstrate a machine learning based adaptive optics method to recover the doughnut-shaped focus with high speed. In our method, the relationship between the distorted doughnut-shaped intensity point spread function and the coefficients of the first 15 Zernikc modes for phase correction is established. Experimental results show that the wavefront aberration with 101,784 optical control elements can be predicted within similar to 17 ms even using a personal computer. and 97.5% correction accuracy can be achieved in 200 repeated tests. Besides, we successfully apply this method in the scanning microscopy theoretically. With a large number of optical control elements and fast operation speed, our method may pave the way for many important applications in bioimaging, such as deep tissue stimulated emission depletion (STED) microscopy. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement