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 graphics processing unit (GPU)-accelerated parallel algorithm based on phase diversity images. We apply an approximate model for the point spread function (PSF) 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 1300x, 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 linear quadratic regulator (LQR) controller into the adaptive optics (AO) 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.