Real-time wavefront compensation based on deep learning using single far-field intensity image

Jiang You; Zhenjiao Xiang; Min Wan; Jingliang Gu; Zongjia Shi; Yinglei Du

doi:10.1117/12.2604727

24 November 2021 Real-time wavefront compensation based on deep learning using single far-field intensity image

Jiang You, Zhenjiao Xiang, Min Wan, Jingliang Gu, Zongjia Shi, Yinglei Du

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 1206908 (2021) https://doi.org/10.1117/12.2604727
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

To improve the laser beam quality affected by atmospheric turbulence, adaptive optics (AO) is needed. Nowadays, using simple AO systems to achieve low-latency and accurate beam purification is a hot spot in the direction of AO technology. The traditional AO system is greatly simplified by wavefront sensor-less (WFS-less) AO. However, the current WFS-less methods generally have a serious problem that search algorithms is used, which means a lot of iterative calculations and measurements are needed, and they can result in high latency. In order to solve this problem, this article proposes a novel compensation technique based on deep learning and eigenmodes of deformable mirrors (DM), which can obtain the compensation voltages directly from single frame of far-field intensity images. Compared with the existing WFS-less methods, it does not require any iterative operations and has good real-time performance. A convolutional neural network (CNN) model is built in this article, which takes 224*224 far-field intensity images as input and 67-dimensional eigenmode coefficients of DM as output. We completed the closed-loop experiment based on this method, and almost achieved the same result as the closed-loop correction based on the Shack-Hartmann Wavefront Sensor (HSWS).

Citation Download Citation

Jiang You, Zhenjiao Xiang, Min Wan, Jingliang Gu, Zongjia Shi, and Yinglei Du "Real-time wavefront compensation based on deep learning using single far-field intensity image", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 1206908 (24 November 2021); https://doi.org/10.1117/12.2604727

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