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Astronomical Image Colorization and Up-scaling with Conditional Generative Adversarial Networks

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dc.contributor.authorKalvankar,Shreyas
dc.contributor.authorPandit,Hrushikesh
dc.contributor.authorParwate,Pranav
dc.contributor.authorPatil,Atharva
dc.contributor.authorKamalapur,Snehal
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:28Z
dc.date.available2022-09-28T17:10:28Z
dc.date.issued2022
dc.description.abstractThis research aims to provide an automated approach for the problem of Image colorization and Single Image Super Resolution by focusing on a very specific domain: astronomical images, using Generative Adversarial Networks. We explore the usage of various models in RBG and L*a*b color spaces. We use transfer learning owing to a small data set, using pre-trained ResNet-18 as a backbone encoder and fine-tune it further. The model produces visually appealing images that are high resolution and colorized. We present our results by evaluating the GANs quantitatively using distance metrics such as L1 distance and L2 distance in each of the color spaces across all channels to provide a comparative analysis. We use Fréchet inception distance (FID) to compare the distribution of the generated images and real image to assess the model's performance.en
dc.identifier.doi10.18420/inf2022_40
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39540
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectAstronomical images
dc.subjectGenerative Adversarial Networks
dc.subjectImage Colorization
dc.subjectSingle Image Super Resolution
dc.titleAstronomical Image Colorization and Up-scaling with Conditional Generative Adversarial Networksen
gi.citation.endPage498
gi.citation.startPage489
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitleWorkshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro)

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften