Deep learning wavefront sensing

We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of image-based wavefront sensing architectures that can directly estimate Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. We also demonstrated that the proposed deep learning wavefront sensor can be trained to estimate wavefront aberrations stimulated by a point source and even extended sources.

# Adaptive optics and wavefront sensing # Physics and Astronomy # Digital Holography and Microscopy # Astronomical Observations and Instrumentation # Engineering

Yohei Nishizaki, Matias Valdivia, Ryoichi Horisaki, et al. (2019). "Deep learning wavefront sensing". Optics Express. https://doi.org/10.1364/oe.27.000240