Abstract
Mathematical modelling methods have several limitations when addressing complex physics whose calculations require considerable amount of time. This is the case of adaptive optics, a series of techniques used to process and improve the resolution of astronomical images acquired from ground-based telescopes due to the aberrations introduced by the atmosphere. Usually, with adaptive optics the wavefront is measured with sensors and then reconstructed and corrected by means of a deformable mirror. An improvement in the reconstruction of the wavefront is presented in this work, using convolutional neural networks (CNN) for data obtained from the Tomographic Pupil Image Wavefront Sensor (TPI-WFS). The TPI-WFS is a modified curvature sensor, designed for measuring atmospheric turbulences with defocused wavefront images. CNNs are well-known techniques for its capacity to model and predict complex systems. The results obtained from the presented reconstructor, named Convolutional Neural Networks in Defocused Pupil Images (CRONOS), are compared with the results of Wave-Front Reconstruction (WFR) software, initially developed for the TPI-WFS measurements, based on the least-squares fit. The performance of both reconstruction techniques is tested for 153 Zernike modes and with simulated noise. In general, CRONOS showed better performance than the reconstruction from WFR in most of the turbulent profiles, with significant improvements found for the most turbulent profiles; overall, obtaining around 7% of improvements in wavefront restoration, and 18% of improvements in Strehl.
Highlights
Adaptive optics (AO) is one fundamental technique used for improving the quality of images taken from grounded telescopes, and one of the key mechanisms for actual large telescopes
The results from the reconstruction with Convolutional Neural Networks in Defocused Pupil Images (CRONOS) are compared with the Wave-Front Reconstruction (WFR) reconstruction using, as optical measurements, mean structural similarity (MSSIM) and Strehl ratio [49]
The results provided by the convolutional neural networks (CNN) applied in the most turbulent cases improve the obtained with the WFR reconstructor
Summary
Adaptive optics (AO) is one fundamental technique used for improving the quality of images taken from grounded telescopes, and one of the key mechanisms for actual large telescopes. As an alternative to classical SH sensors, the Tomographic Pupil Image Wavefront Sensor (TPI-WFS) was developed, as a modified curvature sensor [2]. This new sensor has proven successful at measuring the turbulence of the atmosphere, presenting some advantages such as, for example, better quality than a SH sensor when considering low light illumination regime; it is more stable when changes in the optical parameters are introduced [3,4]. One of the techniques used to deal with this huge amount of information is artificial neural networks (ANN), used in several science branches for modeling complex systems. Several artificial intelligence approaches to AO have been developed recently, as in [12], or those reviewed in [13]
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