Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 6, 798(2023)

High-precision turbulence wavefront reconstruction based on Transformer structure

Jia-hao FENG1,2, Qi-li HU3, Lü JIANG1,2, Yan-yan YANG1,2, Sheng-xiao HUA1,2, Jing-jing WU1,2, and Li-fa HU1,2、*
Author Affiliations
  • 1College of Science,Jiangnan University,Wuxi 214122,China
  • 2Jiangsu Provincial Research Center of Light Industry Optoelectronic Engineering and Technology,Wuxi 214122,China
  • 3Key Laboratory of Electro-Optical Countermeasure Test & Evaluation Technology,Luoyang 471003,China
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    The dynamically changing atmospheric turbulence and the reduced brightness of the observed target severely affect the accuracy of the Shack-Hartmann wavefront sensor (SHWFS) to detect wavefronts. Under these two complicated observational conditions, this paper proposes a neural network model based on Transformer structure,which has excellent global modelling capabilities and could reconstruct wavefronts from light spot array images from SHWFS with high accuracy. The residual wavefront RMS error of the presented network model can be stabilized between 0.010 μm and 0.024 μm by simulating for dynamically varying typical atmospheric turbulence coherence length r0. Comparing with reported methods, the wavefront aberrations can be reconstructed more accurately. In addition, the reconstruction accuracy of the method is robust to the magnitude variation of guide stars or detection targets. Therefore, the reconstruction accuracy of this method has strong stability to the changes of two observation conditions, and provides a promising way for high-resolution imaging for large-aperture astronomical optical telescopes.

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    Jia-hao FENG, Qi-li HU, Lü JIANG, Yan-yan YANG, Sheng-xiao HUA, Jing-jing WU, Li-fa HU. High-precision turbulence wavefront reconstruction based on Transformer structure[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(6): 798

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    Paper Information

    Category: Research Articles

    Received: Feb. 21, 2023

    Accepted: --

    Published Online: Jun. 29, 2023

    The Author Email: Li-fa HU (hulifa@jiangnan.edu.cn)

    DOI:10.37188/CJLCD.2023-0067

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