Observations of the Sun provide unique insights into its structure, evolution, and activity, with significant implications for space weather forecasting and solar energy technologies. Ground-based telescopes offer cost-effective and flexible solutions for high-resolution solar observations, but image quality can be affected by atmospheric turbulence. Adaptive optics (AO) systems equipped with Shack–Hartmann wave front sensors (SH-WFS) enable real-time image correction to mitigate these effects. The accuracy of SH-WFS relies on correlation algorithms that measure wave front shifts, but reaching consistent conclusions regarding their accuracy remains challenging. In this study, we conducted an evaluation and comparison of standard correlation algorithms (the Square Difference Function, Normalized Cross-Correlation, Absolute Difference Function, Absolute Difference Function-Squared, and the Covariance Function in the frequency domain (CFF)) using simulated and authentic solar images. We optimized the algorithms through pre-processing techniques and carefully selected the most suitable window function for the CFF algorithm. Additionally, we analyzed the influence of various factors, such as shift ranges, bias, and the size of live images on the accuracy of algorithms. The consistent findings revealed that the CFF algorithm demonstrates superior measurement accuracy and robustness compared to the others. Choosing the CFF algorithm for solar observations can significantly enhance measurement accuracy, AO system performance, and the overall quality of solar research findings, thereby providing crucial support for space weather forecasting and other related scientific fields.

中文翻译：

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Solar Shack-Hartmann 波前传感器图像偏移测量算法的比较分析


对太阳的观测提供了对其结构、演化和活动的独特见解，对空间天气预报和太阳能技术具有重大影响。地面望远镜为高分辨率太阳观测提供了经济高效且灵活的解决方案，但图像质量可能会受到大气湍流的影响。配备 Shack–Hartmann 波前传感器 (SH-WFS) 的自适应光学 (AO) 系统可实现实时图像校正，以减轻这些影响。 SH-WFS 的准确性依赖于测量波前位移的相关算法，但就其准确性得出一致的结论仍然具有挑战性。在本研究中，我们使用模拟和对比方法对标准相关算法（平方差函数、归一化互相关、绝对差函数、绝对差函数平方和频域协方差函数 (CFF)）进行了评估和比较。真实的太阳图像。我们通过预处理技术优化了算法，并为CFF算法精心选择了最合适的窗函数。此外，我们还分析了各种因素（例如位移范围、偏差和实时图像的大小）对算法准确性的影响。一致的研究结果表明，与其他算法相比，CFF 算法表现出卓越的测量精度和鲁棒性。选择CFF算法进行太阳观测可以显着提高测量精度、AO系统性能以及太阳研究成果的整体质量，从而为空间天气预报和其他相关科学领域提供重要支持。