Point spread function

Wavefront

Spherical aberration

Microscopy

Aperture (computer memory)

Zernike polynomials

Adaptive optics

Computer science

Acoustics

Deformable mirror

Physics

Lens (geology)

Optics

Optical aberration

Artificial intelligence

Artificial neural network

Strehl ratio

Optics Express

Adaptive optics relies on the fast and accurate determination of aberrations but is often hindered by wavefront sensor limitations or lengthy optimization algorithms. Deep learning by artificial neural networks has recently been shown to provide determination of aberration coefficients from various microscope metrics. Here we numerically investigate the direct determination of aberration functions in the pupil plane of a high numerical aperture microscope using an artificial neural network. We show that an aberration function can be determined from fluorescent guide stars and used to improve the Strehl ratio without the need for reconstruction from Zernike polynomial coefficients.

Direct determination of aberration functions in microscopy by an artificial neural network