Representation and restoration of atmospheric turbulence-induced phase aberration via principal component analysis

Representation and restoration of phase aberrations are crucial for addressing atmosphere turbulence involved light propagation. However, traditional restoration algorithms, especially those based on Zernike polynomials (ZPs), often encounter challenges related to high computational complexity and the insufficient capture of high-frequency components of phase aberration. This paper introduces Principal Component Analysis (PCA) to extract dominated components of atmospheric turbulence induced phase aberration and compared the restoration performance with traditional ZPs method. The results show that for the same turbulence strength, the accuracy of the restoration using PCA is evidently higher than that using the traditional ZPs method, especially in challenging situations such as strong turbulence, indicating that PCA method can be served as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberration. These findings pave a way to use principal components of phase aberration 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.