Optical imaging quality can be severely degraded by system and sample induced
aberrations. Existing adaptive optics systems typically rely on iterative
search algorithm to correct for aberrations and improve images. This study
demonstrates the application of convolutional neural networks to characterise
the optical aberration by directly predicting the Zernike coefficients from two
to three phase-diverse optical images. We evaluated our network on 600,000
simulated Point Spread Function (PSF) datasets randomly generated within the
range of -1 to 1 radians using the first 25 Zernike coefficients. The results
show that using only three phase-diverse images captured above, below and at
the focal plane with an amplitude of 1 achieves a low RMSE of 0.10 radians on
the simulated PSF dataset. Furthermore, this approach directly predicts Zernike
modes simulated extended 2D samples, while maintaining a comparable RMSE of
0.15 radians. We demonstrate that this approach is effective using only a
single prediction step, or can be iterated a small number of times. This simple
and straightforward technique provides rapid and accurate method for predicting
the aberration correction using three or less phase-diverse images, paving the
way for evaluation on real-world dataset.

使用卷积神经网络直接从两到三个相位多样性光学图像中预测泽尼克系数，对光学像差进行表征，以实现快速准确的相位多样性方法，为真实数据集的评估铺平了道路。

使用深度学习从点扩散函数和扩展图像直接预测泽尼克系数

Direct Zernike Coefficient Prediction from Point Spread Functions and  Extended Images using Deep Learning

As we look to the next generation of adaptive optics systems, now is the time
to develop and explore the technologies that will allow us to image rocky
Earth-like planets; wavefront control algorithms are not only a crucial
component of these systems, but can benefit our adaptive optics systems without
requiring increased detector speed and sensitivity or more effective and
efficient deformable mirrors. To date, most observatories run the workhorse of
their wavefront control as a classic integral controller, which estimates a
correction from wavefront sensor residuals, and attempts to apply that
correction as fast as possible in closed-loop. An integrator of this nature
fails to address temporal lag errors that evolve over scales faster than the
correction time, as well as vibrations or dynamic errors within the system that
are not encapsulated in the wavefront sensor residuals; these errors impact
high contrast imaging systems with complex coronagraphs. With the rise in
popularity of machine learning, many are investigating applying modern machine
learning methods to wavefront control. Furthermore, many linear implementations
of machine learning methods (under varying aliases) have been in development
for wavefront control for the last 30-odd years. With this work we define
machine learning in its simplest terms, explore the most common machine
learning methods applied in the context of this problem, and present a review
of the literature concerning novel machine learning approaches to wavefront
control.

研究了自适应光学系统中的波前控制算法、探索了图像地球般岩石行星的技术，并回顾了机器学习方法在波前控制方面的应用。