arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.10241astro-ph.IMastro-ph.GAastro-ph.SRcs.CV

Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks

College of Physics and Optoelectronics, Taiyuan University of Technology(太原理工大学物理与光电工程学院);Department of Physics, Durham University(杜伦大学物理系);College of Information and Computer Science, Taiyuan University of Technology(太原理工大学信息与计算机学院)

Peng Jia, Mingyang Ma, Dongmei Cai, Weihua Wang, Juanjuan Li, Can Li

英文摘要:

The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this paper, we propose a compressive Shack-Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all sub-apertures, our method reconstructs wavefronts with slope measurements of sub-apertures which have spot images with high signal to noise ratio. Besides, we further propose to use a deep neural network to accelerate wavefront reconstruction speed. During the training stage of the deep neural network, we propose to add a drop-out layer to simulate the compressive sensing process, which could increase development speed of our method. After training, the compressive Shack-Hartmann wavefront sensing method can reconstruct wavefronts in high spatial resolution with slope measurements from only a small amount of sub-apertures. We integrate the straightforward compressive Shack-Hartmann wavefront sensing method with image deconvolution algorithm to develop a high-order image restoration method. We use images restored by the high-order image restoration method to test the performance of our the compressive Shack-Hartmann wavefront sensing method. The results show that our method can improve the accuracy of wavefront measurements and is suitable for real-time applications.

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