Wavefront sensor-less adaptive optics using deep reinforcement learning

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. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

Wavefront aberrations can be corrected using adaptive optics, with sensor-less adaptive optics utilizing image information directly for correction. A Deep Reinforcement Learning (DRL) approach has been demonstrated to improve the performance of adaptive optics correction.