Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection

We present a learning-based Shack-Hartmann wavefront sensor (SHWS) to achieve the high-order aberration detection without image segmentation or centroid positioning. Zernike coefficient amplitudes of aberrations measured from biological samples are referred and expanded to generate the training datasets. With one SHWS pattern inputted, up to 120th Zernike modes could be predicted within 10.9 ms with 95.56% model accuracy by a personal computer. The statistical experimental results show that compared with traditional modal-based SHWS, the root mean squared error in phase residuals of this method is reduced by similar to 40.54% and the Strehl ratio of the point spread functions is improved by similar to 27.31%. The aberration detection performance of this method is also validated on a mouse brain slice with 300 mu m thickness and the median improvement of peak-to-background ratio of this method is similar to 30% to 40% compared with traditional SHWS. With the high detection accuracy, simple processes, fast prediction speed and good compatibility, this work offers a potential approach to improve the wavefront sensing ability of SHWS, which could be combined with an existing adaptive optics system and be further applied in biological applications. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement