Compensating atmospheric turbulence with convolutional neural networks for defocused pupil image wave-front sensors

Abstract

Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work are presented computational results from a modified curvature sensor, the Tomographic Pupil Image Wave-front Sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional Neural Networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (root mean square error, mean structural similarity and Strehl ratio). Two different scenarios are set, attending to different resolutions for the reconstruction. The reconstructed wave-fronts from both techniques are compared for wave-fronts of 25 Zernike modes and 153 Zernike modes. In general, CNN trained as reconstructor showed better performance than the reconstruction in TPI-WFS for most of the turbulent profiles, but the most significant improvements were found for higher turbulent profiles that have the lowest r0 values.