Convolution- and Fourier-transform-based reconstructors for pyramid wavefront sensor.

In this paper, we present two novel algorithms for wavefront reconstruction from pyramid-type wavefront sensor data. An overview of the current state-of-the-art in the application of pyramid-type wavefront sensors shows that the novel algorithms can be applied in various scientific fields such as astronomy, ophthalmology, and microscopy. Assuming a computationally very challenging setting corresponding to the extreme adaptive optics (XAO) on the European Extremely Large Telescope, we present the results of the performed end-to-end simulations and compare the achieved AO correction quality (in terms of the long-exposure Strehl ratio) to other methods, such as matrix-vector multiplication and preprocessed cumulative reconstructor with domain decomposition. Also, we provide a comparison in terms of applicability and computational complexity and closed-loop performance of our novel algorithms to other methods existing for this type of sensor.