Emerging Technology From the arXiv: “Astrophotography is currently undergoing a revolution thanks to the increased availability of high quality digital cameras and the software available to process the pictures after they have been taken.
Since photographs of the night sky are almost always better with long exposures that capture more light, this processing usually involves combining several images of the same part of the sky to produce one with a much longer effective exposure.
That’s all straightforward if you’ve taken the pictures yourself with the same gear under the same circumstances. But astronomers want to do better.
“The astrophotography group on Flickr alone has over 68,000 images,” say Dustin Lang at Carnegie Mellon University in Pittsburgh and a couple of pals. These and other images represent a vast source of untapped data for astronomers.
The problem is that it’s hard to combine images accurately when little is known about how they were taken. Astronomers take great care to use imaging equipment in which the pixels produce a signal that is proportional to the number of photons that hit.
But the same cannot be said of the digital cameras widely used by amateurs. All kinds of processes can end up influencing the final image.
So any algorithm that combines them has to cope with these variations. “We want to do this without having to infer the (possibly highly nonlinear) processing that has been applied to each individual image, each of which has been wrecked in its own loving way by its creator,” say Lang and co.
Now, these guys say they’ve cracked it. They’ve developed a system that automatically combines images from the same part of the sky to increase the effective exposure time of the resulting picture. And they say the combined images can rival those from much professional telescopes.
They’ve tested this approach by downloading images of two well-known astrophysical objects: the NGC 5907 Galaxy and the colliding pair of galaxies—Messier 51a and 51b.
For NGC 5907, they ended up with 4,000 images from Flickr, 1,000 from Bing and 100 from Google. They used an online system called astrometry.net that automatically aligns and registers images of the night sky and then combined the images using their new algorithm, which they call Enhance.
The results are impressive. They say that the combined images of NGC5907 (bottom three images) show some of the same faint features that revealed a single image taken over 11 hours of exposure using a 50 cm telescope (the top left image). All the images reveal the same kind of fine detail such as a faint stellar stream around the galaxy.
The combined image for the M51 galaxies is just as impressive, taking only 40 minutes to produce on a single processor. It reveals extended structures around both galaxies, which astronomers know to be debris from their gravitational interaction as they collide.
Lang and co say these faint features are hugely important because they allow astronomers to measure the age, mass ratios, and orbital configurations of the galaxies involved. Interestingly, many of these faint features are not visible in any of the input images taken from the Web. They emerge only once images have been combined.
One potential problem with algorithms like this is that they need to perform well as the number of images they combine increases. It’s no good if they grind to a halt as soon as a substantial amount of data becomes available.
On this score, Lang and co say astronomers can rest easy. The performance of their new Enhance algorithm scales linearly with the number of images it has to combine. That means it should perform well on large datasets.
The bottom line is that this kind of crowd-sourced astronomy has the potential to make a big impact, given that the resulting images rival those from large telescopes.
And it could also be used for historical images, say Lang and co. The Harvard Plate Archives, for example, contain half a million images dating back to the 1880s. These were all taken using different emulsions, with different exposures and developed using different processes. So the plates all have different responses to light, making them hard to compare.
That’s exactly the problem that Lang and co have solved for digital images on the Web. So it’s not hard to imagine how they could easily combine the data from the Harvard archives as well….”
Ref: arxiv.org/abs/1406.1528 : Towards building a Crowd-Sourced Sky Map
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