Researchers have developed a computer vision-based approach to sharpening images captured by ground-based telescopes.
The light emanating from distant stars, planets, and galaxies travels a very long way to reach Earth. The light’s journey may encounter different gravitational effects, which can warp the light as observed from Earth. However, the light must also travel through Earth’s atmosphere before it reaches ground-based telescopes.
The resulting blur is a significant problem for astrophysicists analyzing images for important cosmological data. The apparent shape of galaxies sheds light on large-scale cosmological structures and their gravitational effects. Scientists use numerous software-based approaches to process images to make them sharper and more useful. However, the new computer-vision algorithm,, approach promises better and faster results. The algorithm the team built has been explicitly adapted for processing astronomical images captured by ground-based telescopes, the first time such AI technology has been used for this purpose.
“Photography’s goal is often to get a pretty, nice-looking image,” says Northwestern’s Emma Alexander, the study’s senior author. “But astronomical images are used for science. By cleaning up images in the right way, we can get more accurate data. The algorithm removes the atmosphere computationally, enabling physicists to obtain better scientific measurements. At the end of the day, the images do look better as well.
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