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Intelligent wavefront correction via self-supervised predictive Zernike phase inversion network
Optics Express
|February 20, 2026
Summary
This study introduces a self-supervised predictive Zernike phase inversion network (SS-PZPIN) for optical turbulence correction. The novel method improves phase correction accuracy and fidelity in adaptive optics and free-space optical communication.
Area of Science:
- Optics and Photonics
- Machine Learning
- Optical Communication
Background:
- Dynamic atmospheric turbulence causes phase distortions challenging optical systems.
- Conventional adaptive optics and supervised learning methods struggle with rapid, unpredictable phase variations.
Purpose of the Study:
- To develop a self-supervised learning framework for accurate wavefront correction.
- To enable real-time compensation for atmospheric turbulence in optical systems.
Main Methods:
- Proposed a self-supervised predictive Zernike phase inversion network (SS-PZPIN).
- Integrated dual-intensity Fresnel-consistent inversion and temporal prediction.
- Employed a physics-informed learning framework without labeled phase data.
Main Results:
- Achieved over 25% improvement in phase correction accuracy under strong turbulence.
- Maintained over 100% higher fidelity than the Gerchberg-Saxton algorithm with 2.5-5 ms delays.
- Demonstrated fast inference time (2.67 ms) and strong generalization capabilities.
Conclusions:
- SS-PZPIN offers a scalable and interpretable solution for real-time adaptive optics.
- The method enhances turbulence resilience in free-space optical communication systems.
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