一种基于有限采样信息的神经网络模式波前复原方法
By establishing the relationship between the sub-aperture information and Zernike coefficient of the Shaker-Hartmann wavefront sensor through a neural network model, the problem of reduced wavefront detection accuracy caused by factors such as turbulence is solved, and high-precision wavefront reconstruction is achieved, which is suitable for wavefront measurement in complex environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INST OF OPTICS & ELECTRONICS CHINESE ACAD OF SCI
- Filing Date
- 2023-02-17
- Publication Date
- 2026-07-17
AI Technical Summary
Under the influence of atmospheric turbulence, intensity scintillation, and other factors, the intensity of some sub-aperture light spots of the Shaker-Hartmann wavefront sensor is weak or buried in noise, resulting in a decrease in wavefront detection accuracy. Traditional wavefront reconstruction algorithms are unable to accurately reconstruct the wavefront.
A nonlinear relationship between the detectable sub-aperture information and the Zernike coefficient of the Shaker-Hartmann wavefront sensor is established based on a neural network. Through neural network model training and optimization, high-precision wavefront reconstruction is achieved using limited sampling information.
High-precision wavefront measurement was achieved under partial aperture light-deficient conditions, improving wavefront reconstruction accuracy and reducing information redundancy. It is suitable for high-precision wavefront detection in strong turbulence and scintillation environments.
Smart Images
Figure CN116124304B_ABST