This paper shows that a simple convolutional neural network (CNN) can be used to build an object-agnostic wavefront sensor. Using the well-known Phase Diversity approach as a point of departure, Fourier-space metrics are computed from the conventional and diversity images and then fed to the CNN, which predicts values of the underlying Zernike coefficients. The methodology is shown to work in the presence of Gaussian noise. Prediction errors for defocus, astigmatism, and spherical are on the order of 1/100 of the wavelength.
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