Learning to denoise astronomical images with U-nets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F21%3A00123743" target="_blank" >RIV/00216224:14310/21:00123743 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1093/mnras/staa3567" target="_blank" >https://doi.org/10.1093/mnras/staa3567</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/mnras/staa3567" target="_blank" >10.1093/mnras/staa3567</a>
Alternative languages
Result language
angličtina
Original language name
Learning to denoise astronomical images with U-nets
Original language description
Y Astronomical images are essential for exploring and understanding the Universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope (HST), are heavily oversubscribed in the Astronomical Community. Images also often contain additive noise, which makes denoising a mandatory step in post-processing the data before further data analysis. In order to maximize the efficiency and information gain in the post-processing of astronomical imaging, we turn to machine learning. We propose ASTRO U-NET, a convolutional neural network for image denoising and enhancement. For a proof-of-concept, we use HST images from Wide Field Camera 3 instrument UV/visible channel with F555W and F606W filters. Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. From these images, we are able to recover 95.9 per cent of stars with an average flux error of 2.26 per cent. Furthermore, the images have, on average, 1.63 times higher signal-to-noise ratio than the input noisy images, equivalent to the stacking of at least three input images, which means a significant reduction in the telescope time needed for future astronomical imaging campaigns.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10308 - Astronomy (including astrophysics,space science)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Monthly Notices of the Royal Astronomical Society
ISSN
0035-8711
e-ISSN
1365-2966
Volume of the periodical
503
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
3204-3215
UT code for WoS article
000649000600006
EID of the result in the Scopus database
2-s2.0-85110340238