Atmospheric turbulence induced phase distortions: the principal component analysis and optimized correction strategy

Area of Science:

• Optics and Photonics
• Astronomy and Astrophysics
• Image Processing

Background:

• Atmospheric turbulence causes phase aberrations in optical systems, degrading image quality.
• Adaptive optics systems are crucial for correcting these distortions, but often face trade-offs between accuracy and computational efficiency.

Purpose of the Study:

• To introduce a novel method for correcting atmospheric turbulence-induced phase aberrations using principal component analysis (PCA).
• To compare the effectiveness of PCA against traditional Zernike polynomials (ZPs) for phase correction.
• To provide data references for deploying adaptive optics models in diverse turbulent environments.

Main Methods:

• Phase aberration correction using principal component analysis (PCA).
• Comparison of PCA with Zernike polynomials (ZPs) in terms of error reduction and term sensitivity.
• Analysis of performance under varying turbulence conditions (Kolmogorov spectrum satisfied or not).

Main Results:

• PCA effectively corrects atmospheric turbulence phase distortions, outperforming ZPs.
• PCA demonstrated higher sensitivity, achieving over 67% error reduction from the 3rd to 4th term, compared to ZPs' <26%.
• PCA requires fewer terms for higher accuracy, particularly in strong turbulence, and provides data for model deployment.

Conclusions:

• PCA offers a more accurate and computationally efficient solution for wavefront sensor-less adaptive optics.
• The proposed PCA method addresses the accuracy-efficiency trade-off, enabling faster and more precise intelligent correction.
• This study lays the foundation for advanced adaptive optics systems in challenging optical environments.