Royal Astronomical Society | 2017 Feb 22
[img3="The frames here show an example of an original galaxy image (left), the same image deliberately degraded (second from left), the image after recovery with the neural net (second from right), and the image processed with deconvolution, the best existing technique (right). Credit: K. Schawinski / C. Zhang / ETH Zurich.Telescopes, the workhorse instruments of astronomy, are limited by the size of the mirror or lens they use. Using 'neural nets', a form of artificial intelligence, a group of Swiss researchers now have a way to push past that limit, offering scientists the prospect of the sharpest ever images in optical astronomy. ...
Click here for a full size image (~12 Mb)"]https://www.ras.org.uk/images/stories/p ... _small.png[/img3][hr][/hr]
The diameter of its lens or mirror, the so-called aperture, fundamentally limits any telescope. In simple terms, the bigger the mirror or lens, the more light it gathers, allowing astronomers to detect fainter objects, and to observe them more clearly. A statistical concept known as 'Nyquist sampling theorem' describes the resolution limit, and hence how much detail can be seen.
The Swiss study, led by Prof Kevin Schawinski of ETH Zurich, uses the latest in machine learning technology to challenge this limit. They teach a neural network, a computational approach that simulates the neurons in a brain, what galaxies look like, and then ask it to automatically recover a blurred image and turn it into a sharp one. Just like a human, the neural net needs examples – in this case a blurred and a sharp image of the same galaxy – to learn the technique.
Their system uses two neural nets competing with each other, an emerging approach popular with the machine learning research community called a "generative adversarial network", or GAN. The whole teaching programme took just a few hours on a high performance computer. ...
Generative Adversarial Networks recover features in astrophysical images
of galaxies beyond the deconvolution limit - Kevin Schawinski et al
- Monthly Notices of the RAS: Letters 467(1):L110 (May 2017) DOI: 10.1093/mnrasl/slx008
arXiv.org > astro-ph > arXiv:1702.00403 > 01 Feb 2017
Additional Information:
[list]
NIPS 2016 Tutorial: Generative Adversarial Networks - Ian Goodfellow
[list]arXiv.org > cs > arXiv:1701.00160 > 31 Dec 2016 (v1), 09 Jan 2017 (v3) [/list]
Generative Adversarial Networks - Ian Goodfellow et al
[list]arXiv.org > stat > arXiv:1406.2661 > 10 Jun 2014 [/list][/list]