Segmented primary mirror provides many crucial important advantages for the construction of extra-large space telescopes. The imaging quality of this class of telescope is susceptible to phasing error between primary mirror segments. Deep learning has been widely applied in the field of optical imaging and wavefront sensing, including phasing segmented mirrors. Compared to other image-based phasing techniques, such as phase retrieval and phase diversity, deep learning has the advantage of high efficiency and free of stagnation problem. However, at present deep learning methods are mainly applied to coarse phasing and used to estimate piston error between segments. In this paper, deep Bi-GRU neural work is introduced to fine phasing of segmented mirrors, which not only has a much simpler structure than CNN or LSTM network, but also can effectively solve the gradient vanishing problem in training due to long term dependencies. By incorporating phasing errors (piston and tip-tilt errors), some low-order aberrations as well as other practical considerations, Bi-GRU neural work can effectively be used for fine phasing of segmented mirrors. Simulations and real experiments are used to demonstrate the accuracy and effectiveness of the proposed methods.

中文翻译：

---

用于分段反射镜精细定相的深度学习波前传感

分段主镜为超大型太空望远镜的建造提供了许多关键的重要优势。这类望远镜的成像质量容易受到主镜段之间的相位误差的影响。深度学习已广泛应用于光学成像和波前传感领域，包括相位分割镜。与其他基于图像的相位技术相比，如相位检索和相位多样性，深度学习具有效率高、无停滞问题的优势。然而，目前深度学习方法主要应用于粗调相，用于估计节段之间的活塞误差。在本文中，深度 Bi-GRU 神经工作被引入到分割镜的精细定相中，它不仅具有比 CNN 或 LSTM 网络简单得多的结构，还能有效解决训练中由于长期依赖导致的梯度消失问题。通过结合相位误差（活塞和倾斜误差）、一些低阶像差以及其他实际考虑，Bi-GRU 神经工作可以有效地用于分割镜的精细相位。仿真和真实实验被用来证明所提出方法的准确性和有效性。