Real-time wavefront measurement based on GPU-accelerated parallel algorithm using phase diversity images

In this study, we introduce a novel real-time measurement and correction method for time-varying wavefront aberrations. Central to this method is a GPU-accelerated parallel algorithm based on phase diversity images. We apply an approximate model for the point spread function to reduce the computatio...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 73; s. 1
Hlavní autoři: Kim, Jinsung, Lee, Kye-Sung, Lee, Sang-Chul, Kim, Dong Uk, Kim, I Jong, Chang, Ki Soo, Hur, Hwan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Aberration
Acceleration
Adaptive optics
Algorithms
Astigmatism
Computational modeling
Diversity methods
Global optimization
GPU acceleration
gradient-based optimization algorithm
Graphics processing units
image-based wavefront sensing
Linear quadratic regulator
Measurement
Optimization
Phase diversity
phase diversity method
point spread function
Point spread functions
Pupils
Real time
real-time aberration measurement
Real-time systems
Time measurement
Wave fronts
ISSN:0018-9456, 1557-9662
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Shrnutí:In this study, we introduce a novel real-time measurement and correction method for time-varying wavefront aberrations. Central to this method is a GPU-accelerated parallel algorithm based on phase diversity images. We apply an approximate model for the point spread function to reduce the computational load of error metric minimization. We forge a parallel framework that independently measures each aberration mode by deriving an object-independent error metric and its gradient. Numerical experiments with actual Kolmogorov model-based data were conducted to assess the measurement performance and real-time feasibility of the proposed method. When juxtaposed with the global optimization algorithm, the proposed method improved the computation speed by up to 1300 times, while maintaining measurement accuracy. Moreover, we executed benchmark tests on diverse hardware configurations, thereby verifying the real-time viability of GPU acceleration. The GPU achieved a 6.8x improvement in computational speed compared to the CPU. Seamlessly integrating a LQR controller into the adaptive optics system, we zeroed in on the real-time correction of dynamic aberrations. The empirical results exhibited an operational speed of 90 [Hz] in a realistic environment for correcting only three types of aberrations (astigmatism, defocus, and coma). Furthermore, we demonstrated the correction capability for large-scale aberrations, proving that the proposed method is scalable relative to the intensity of aberrations. In conclusion, this study paves the way for a combination of real-time execution and precise wavefront aberration correction in a sensorless AO, establishing a novel standard for future development and enhancements in wavefront sensing technology.
Bibliografie:ObjectType-Article-1
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3341104