Wavefront sensor-less adaptive optics using deep reinforcement learning

doi:10.1364/boe.427970

Wavefront sensor-less adaptive optics using deep reinforcement learning

2021-08-06

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DOI：10.1364/boe.427970

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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.

Wavefront sensor-less adaptive optics using deep reinforcement learning

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