AI algorithm unblurs the cosmos
Tool produces faster, more realistic images than current methods

Date:
March 31, 2023
Source:
Northwestern University
Summary:
Researchers adapted a well-known computer-vision algorithm
used for sharpening photos and, for the first time, applied
it to astronomical images from ground-based telescopes. While
astrophysicists already use technologies to remove blur, the adapted
AI-driven algorithm works faster and produces more realistic images
than current technologies. The resulting images are blur-free and
truer to life.


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FULL STORY ==========================================================================
The cosmos would look a lot better if Earth's atmosphere wasn't photo
bombing it all the time.


==========================================================================
Even images obtained by the world's best ground-based telescopes are
blurry due to the atmosphere's shifting pockets of air. While seemingly harmless, this blur obscures the shapes of objects in astronomical images, sometimes leading to error-filled physical measurements that are essential
for understanding the nature of our universe.

Now researchers at Northwestern University and Tsinghua University in
Beijing have unveiled a new strategy to fix this issue. The team adapted
a well-known computer-vision algorithm used for sharpening photos and,
for the first time, applied it to astronomical images from ground-based telescopes. The researchers also trained the artificial intelligence (AI) algorithm on data simulated to match the Vera C. Rubin Observatory's
imaging parameters, so, when the observatory opens next year, the tool
will be instantly compatible.

While astrophysicists already use technologies to remove blur, the
adapted AI- driven algorithm works faster and produces more realistic
images than current technologies. The resulting images are blur-free
and truer to life. They also are beautiful -- although that's not the technology's purpose.

"Photography's goal is often to get a pretty, nice-looking image,"
said 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." The research will be published March 30 in the Monthly Notices
of the Royal Astronomical Society.

Alexander is an assistant professor of computer science at Northwestern's McCormick School of Engineering, where she runs the Bio Inspired
Vision Lab.

She co-led the new study with Tianao Li, an undergraduate in electrical engineering at Tsinghua University and a research intern in Alexander's
lab.

When light emanates from distant stars, planets and galaxies, it travels through Earth's atmosphere before it hits our eyes. Not only does our atmosphere block out certain wavelengths of light, it also distorts the
light that reaches Earth. Even clear night skies still contain moving
air that affects light passing through it. That's why stars twinkle and
why the best ground-based telescopes are located at high altitudes where
the atmosphere is thinnest.

"It's a bit like looking up from the bottom of a swimming pool," Alexander said. "The water pushes light around and distorts it. The atmosphere is,
of course, much less dense, but it's a similar concept." The blur becomes
an issue when astrophysicists analyze images to extract cosmological
data. By studying the apparent shapes of galaxies, scientists can detect
the gravitational effects of large-scale cosmological structures, which
bend light on its way to our planet. This can cause an elliptical galaxy
to appear rounder or more stretched than it really is. But atmospheric
blur smears the image in a way that warps the galaxy shape. Removing
the blur enables scientists to collect accurate shape data.

"Slight differences in shape can tell us about gravity in the universe," Alexander said. "These differences are already difficult to detect. If
you look at an image from a ground-based telescope, a shape might
be warped. It's hard to know if that's because of a gravitational
effect or the atmosphere." To tackle this challenge, Alexander and
Li combined an optimization algorithm with a deep-learning network
trained on astronomical images. Among the training images, the team
included simulated data that matches the Rubin Observatory's expected
imaging parameters. The resulting tool produced images with 38.6% less
error compared to classic methods for removing blur and 7.4% less error compared to modern methods.

When the Rubin Observatory officially opens next year, its telescopes
will begin a decade-long deep survey across an enormous portion of the
night sky.

Because the researchers trained the new tool on data specifically
designed to simulate Rubin's upcoming images, it will be able to help
analyze the survey's highly anticipated data.

For astronomers interested in using the tool, the open-source,
user-friendly code and accompanying tutorials are available online.

"Now we pass off this tool, putting it into the hands of astronomy
experts," Alexander said. "We think this could be a valuable resource
for sky surveys to obtain the most realistic data possible." The study, "Galaxy image deconvolution for weak gravitational lensing with unrolled plug-and-play ADMM," used computational resources from the Computational Photography Lab at Northwestern University.

* RELATED_TOPICS
o Space_&_Time
# Galaxies # Space_Telescopes # Astronomy # Cosmic_Rays
o Computers_&_Math
# Photography # Computers_and_Internet #
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* RELATED_TERMS
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========================================================================== Story Source: Materials provided by Northwestern_University. Original
written by Amanda Morris. Note: Content may be edited for style and
length.


========================================================================== Journal Reference:
1. Tianao Li, Emma Alexander. Galaxy Image Deconvolution for Weak
Gravitational Lensing with Unrolled Plug-and-Play ADMM. Submitted
to arXiv, 2023 DOI: 10.48550/arXiv.2211.01567 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230331120633.htm

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