Li Yuchen, Qin Chao, An Qichang, Xu Zhenbang
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Rep. 2025 Jul 2;15(1):23053. doi: 10.1038/s41598-025-09133-5.
The ability to detect pistons with high accuracy over a wide range is paramount to the co-phasing of sparse aperture optical systems. This paper proposes a global piston error modulation method for sparse aperture mirrors based on convolutional neural networks. The efficacy of this approach is demonstrated by the introduction of a convolutional block attention module (CBAM) with a data generalization mechanism, which facilitates the rapid and accurate learning of key features from actual co-phasing sensor images. This is achieved with less labelled data, thereby enabling the accurate detection of piston error distribution. The experimental results demonstrate that the method exhibits high prediction accuracy, enhances the piston error detection efficiency and sensing range, and facilitates global fine phase correction (<λ/80) under closed-loop conditions. The technique demonstrates considerable potential for application in the field of simplifying the wavefront sensing and modulation process of large segmented telescopes.
在很宽的范围内高精度检测活塞误差对于稀疏孔径光学系统的共相控至关重要。本文提出了一种基于卷积神经网络的稀疏孔径镜全局活塞误差调制方法。通过引入具有数据泛化机制的卷积块注意力模块（CBAM）来证明该方法的有效性，该模块有助于从实际共相控传感器图像中快速准确地学习关键特征。这在使用较少标记数据的情况下得以实现，从而能够精确检测活塞误差分布。实验结果表明，该方法具有较高的预测精度，提高了活塞误差检测效率和传感范围，并有助于在闭环条件下进行全局精细相位校正（<λ/80）。该技术在简化大型拼接望远镜的波前传感和调制过程领域具有相当大的应用潜力。
