Influencing factor analysis of the Principal Component Analysis for the characterization and restoration of phase aberrations due to atmospheric turbulence

Restoration of phase aberrations are crucial for addressing atmospheric turbulence involved light propagation.Traditional Zernike polynomial methods face high computational complexity and poor capture of high-frequency components, so we propose a Principal Component Analysis-based representation method. This paper analyzes factors affecting restoration accuracy, focusing on the size of sample space and sampling interval of D/r0 ,with r0 being the atmospheric coherence length and D being the pupil diameter, Results show PCA outperforms Zernike methods, especially in strong turbulence, and larger sampling intervals improve accuracy with less data.These findings pave a way to use PCs of phase aberrations with less orders than traditional ZPs to achieve data dimensionality reduction, and offer a reference to accelerate and stabilize the model based and deep learning based adaptive optics correction.