Durech Eduard, Newberry William, Franke Jonas, Sarunic Marinko V
School of Engineering Science, 8888 University Dr., Burnaby, BC V5A 1S6, Canada.
Institute of Biomedical Optics, University of Lübeck, 23562 Luebeck, Germany.
Biomed Opt Express. 2021 Aug 6;12(9):5423-5438. doi: 10.1364/BOE.427970. eCollection 2021 Sep 1.
Image degradation due to wavefront aberrations can be corrected with adaptive optics (AO). In a typical AO configuration, the aberrations are measured directly using a Shack-Hartmann wavefront sensor and corrected with a deformable mirror in order to attain diffraction limited performance for the main imaging system. Wavefront sensor-less adaptive optics (SAO) uses the image information directly to determine the aberrations and provide guidance for shaping the deformable mirror, often iteratively. In this report, we present a Deep Reinforcement Learning (DRL) approach for SAO correction using a custom-built fluorescence confocal scanning laser microscope. The experimental results demonstrate the improved performance of the DRL approach relative to a Zernike Mode Hill Climbing algorithm for SAO.
由于波前像差导致的图像退化可以通过自适应光学（AO）进行校正。在典型的AO配置中，使用夏克-哈特曼波前传感器直接测量像差，并通过可变形镜进行校正，以便主成像系统实现衍射极限性能。无波前传感器自适应光学（SAO）直接利用图像信息来确定像差，并为可变形镜的整形提供指导，通常是迭代进行的。在本报告中，我们提出了一种使用定制荧光共聚焦扫描激光显微镜进行SAO校正的深度强化学习（DRL）方法。实验结果表明，相对于用于SAO的泽尼克模式爬山算法，DRL方法具有更高的性能。
