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arXiv:2509.10662 (physics)
[Submitted on 12 Sep 2025 ]

Title: Deep learning-driven adaptive optics for laser wavefront correction

Title: 基于深度学习的自适应光学用于激光波前校正

Authors:Jikai Wang, Sven Burckhard, Sonam Smitha Ravi, Dominik Bauer, Volker Rominger, Stefan Nolte, Daniel Flamm
Abstract: {We report on an intensity-only and deep-learning based method for laser beam characterization that allows to predict the underlying optical field within milliseconds. A simple near-field / far-field camera setup enables online control of an adaptive optics to optimize beam quality. The robustness and precision of the method is enhanced by applying the concept of phase diversity based on spiral phase plates.
Abstract: 我们报告一种基于强度和深度学习的激光束表征方法,该方法可在毫秒内预测潜在的光学场。 简单的近场/远场相机设置使得在线控制自适应光学以优化光束质量成为可能。 通过应用基于螺旋相位板的相位多样性概念,提高了该方法的鲁棒性和精度。
Comments: Accepted manuscript (Applied Optics)
Subjects: Optics (physics.optics)
Cite as: arXiv:2509.10662 [physics.optics]
  (or arXiv:2509.10662v1 [physics.optics] for this version)
  https://doi.org/10.48550/arXiv.2509.10662
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Daniel Flamm [view email]
[v1] Fri, 12 Sep 2025 19:41:53 UTC (4,563 KB)
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