Deep learning wavefront sensing

Yohei Nishizaki, Matias Valdivia, Ryoichi Horisaki, Katsuhisa Kitaguchi, Mamoru Saito, Jun Tanida, Esteban Vera

Research output: Contribution to journalArticlepeer-review

161 Scopus citations


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.

Original languageEnglish
Pages (from-to)240-251
Number of pages12
JournalOptics Express
Issue number1
StatePublished - 7 Jan 2019


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