Deep learning wavefront sensing.

作者：Yohei Nishizaki, Matias Valdivia, R. Horisaki, K. Kitaguchi, Mamoru Saito, J. Tanida, Esteban Vera · 发表于：Optics Express · 年份：2019 · DOI：10.1364/oe.27.000240 · 被引用次数：187 · 研究领域：Medicine、Computer Science

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.