基于主成分分析法的大气湍流相位畸变表征和还原影响因素分析

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Abstract： Restoration phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials （ZPs） ten encounter chalenges related to high computational complexity insufcient capture high-frequency phase aberration components， so we proposed a Principal-Component-Analysis-based method for representing phase aberrations. This paper discusses the factors influencing the accuracy restoration， mainly including the sample space size the sampling interval D/r0 ， on the basis characterizing phase aberrations by Principal Components （PCs）. The experimental results show that a larger D/r0 sampling interval can ensure the generalization ability robustness the principal components in the case a limited amount original data， which can help to achieve high-precision deployment the model in practical applications quickly.In the environment relatively strong turbulence in the test set D/r0=24 ，the use 34 terms PCs can improve the corrected Strehl ratio （SR）from 0.007 to 0.158 5， while the Strehl ratio the light spot after restoration using 34 terms ZPs is only O0.0215， demonstrating almost no correction effect. The results indicate that PCs can serve as a beter alternative in representing restoring the characteristics atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs phase aberrations fewer terms than traditional ZPs to achieve data dimensionality reduction， fer a reference to accelerate stabilize the model deep learning based adaptive optics correction.

words： phase aberration; atmospheric turbulence; principal component analysis; Zernike polynomials

摘要：为了有效表征、还原大气湍流造成的相位畸变，解决传统 Zermike多项式方法引起的相位还原高频信息不足问题，提出了基于主成分分析法的畸变相位特征表征、还原方法，对可能影响主成分精度从而影响还原效果的因素进行研究。（剩余19709字）