  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.
