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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10308 - Astronomy (including astrophysics,space science)

Result continuities

  • Project

  • 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