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Estimation of optical aberrations from volumetric intensity images is a keystep in sensorless adaptive optics for 3D microscopy. Recent approaches basedon deep learning promise accurate results at fast processing speeds. However,collecting ground truth microscopy data for training the network is typicallyvery difficult or even impossible thereby limiting this approach in practice.Here, we demonstrate that neural networks trained only on simulated data yieldaccurate predictions for real experimental images. We validate our approach onsimulated and experimental datasets acquired with two different microscopymodalities, and also compare the results to non-learned methods. Additionally,we study the predictability of individual aberrations with respect to theirdata requirements and find that the symmetry of the wavefront plays a crucialrole. Finally, we make our implementation freely available as open sourcesoftware in Python.

Practical sensorless aberration estimation for 3D microscopy with deep learning