Wavefront sensorless adaptive optics control algorithm based on deep learning

Jing Wang; Bo Chen; Shuai Wang

doi:10.1117/12.2575489

10 October 2020 Wavefront sensorless adaptive optics control algorithm based on deep learning

Jing Wang, Bo Chen, Shuai Wang

Proceedings Volume 11550, Optoelectronic Imaging and Multimedia Technology VII; 115501C (2020) https://doi.org/10.1117/12.2575489
Event: SPIE/COS Photonics Asia, 2020, Online Only

Conference Poster

Abstract

For adaptive optics without wavefront detection, the wavefront control method based on deep learning is analyzed. The simulation model of adaptive optics is established,The far-field spot data collected by the photodetector is used as the input of the neural network model, and the Zernike mode coefficient is used as the output. The fully trained model can quickly and accurately recover and control the low-order wavefront. The simulation results show that convolution neural network can effectively extract image features, which is better than ordinary depth neural network model. For convolution network model, the larger the number of training sets, the smaller the value of loss function after convergence, and the higher the accuracy of the model. Compared with the traditional iterative optimization control method, the control method based on neural network model has obvious advantages in real-time.

Citation Download Citation

Jing Wang, Bo Chen, and Shuai Wang "Wavefront sensorless adaptive optics control algorithm based on deep learning", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 115501C (10 October 2020); https://doi.org/10.1117/12.2575489

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