Deep learning wavefront sensing

doi:10.1364/OE.27.000240

Deep learning wavefront sensing

2019-01-04

200

OA

AI

DOI：10.1364/OE.27.000240

Original

Original request for help

Save

Abstract

En 中文

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. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Keywords:

PHASE

DIVERSITY

SENSOR

Deep learning wavefront sensing

Abstract

Journal

Organization