Data Preprocessing on Sequential Data for Improved Astronomical Imaging

Obtaining diffraction limited astronomical images using ground-based telescopes and sensors operating at optical wavelengths is currently not possible due to the effects of atmospheric turbulence. Adaptive optics is a mature technology that attempts to minimise the effects of atmospheric turbulence through wavefront detection and optical path correction in real-time. Our work involves the acquisition and preprocessing of sequential astronomical data and the application of machine learning algorithms to obtain consistently, high-resolution images called lucky frames. This paper will outline the design of a framework used to capture and preprocess sequential data in real-time, and explore machine learning extensions to accurately predict lucky frames.