Neural network algorithm for under-sampled wavefront reconstruction: mathematical analysis and implementation.

IF 3.2 2区 物理与天体物理 Q2 OPTICS
Optics express Pub Date : 2024-11-04 DOI:10.1364/OE.533183
Zhiyun Zhang, Ruiyan Jin, Fangfang Chai, Zhihao Lei, Linxiong Wen, Shuai Wang, Ping Yang
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引用次数: 0

Abstract

The Shack-Hartmann wavefront sensor (SHWFS) is critical in adaptive optics (AO) for measuring wavefronts via centroid shifts in sub-apertures. Under extreme conditions like strong turbulence or long-distance transmission, wavefront information degrades significantly, leading to undersampled slope data and severely reduced reconstruction accuracy. Conventional algorithms struggle in these scenarios, and existing neural network approaches are not sufficiently advanced. To address this challenge, we propose a mathematically interpretable neural network-based wavefront reconstruction algorithm designed to mitigate the impact of slope loss. Experimental results demonstrate that our algorithm achieves what is believed to be unprecedented fidelity in full-aperture aberration reconstruction with up to 70% wavefront undersampling, representing a precision improvement of approximately 89.3% compared to modal methods. Moreover, the algorithm can be fully trained using simulation data alone, eliminating the need for real data acquisition and significantly enhancing practical applicability.

用于欠采样波前重建的神经网络算法:数学分析与实现。
夏克-哈特曼波前传感器(SHWFS)是自适应光学(AO)中通过子孔径中的中心点偏移测量波前的关键设备。在强湍流或长距离传输等极端条件下,波前信息会明显降低,导致斜率数据采样不足,重建精度严重下降。传统的算法在这些情况下难以奏效,现有的神经网络方法也不够先进。为了应对这一挑战,我们提出了一种基于神经网络的波前重建算法,旨在减轻斜率损失的影响。实验结果表明,我们的算法在全口径像差重建中实现了前所未有的保真度,波前欠采样率高达 70%,与模态方法相比,精度提高了约 89.3%。此外,该算法只需使用模拟数据就能进行完全训练,无需采集真实数据,大大提高了实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Optics express
Optics express 物理-光学
CiteScore
6.60
自引率
15.80%
发文量
5182
审稿时长
2.1 months
期刊介绍: Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.
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