Using Moffat Profiles to Register Astronomical Images

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dc.contributor.authorSchuckman, Mason
dc.contributor.authorProuty, Roy
dc.contributor.authorChapman, David
dc.contributor.authorEngel, Don
dc.date.accessioned2023-06-16T15:21:22Z
dc.date.available2023-06-16T15:21:22Z
dc.date.issued2023-02-15
dc.descriptionEuropean Conference on Computer Vision, Tel Aviv, Israel, October 23–27, 2022.en_US
dc.description.abstractThe accurate registration of astronomical images without a world coordinate system or authoritative catalog is useful for visually enhancing the spatial resolution of multiple images containing the same target. Increasing the resolution of images through super-resolution (SR) techniques can improve the performance of commodity optical hardware, allowing more science to be done with cheaper equipment. Many SR techniques rely on the accurate registration of input images, which is why this work is focused on accurate star finding and registration. In this work, synthetic star field frames are used to explore techniques involving star detection, matching, and transform-fitting. Using Moffat stellar profiles for stars, non-maximal suppression for control-point finding, and gradient descent for point finding optimization, we are able to obtain more accurate transformation parameters than that provided other modern algorithms, e.g., AstroAlign. To validate that we do not over-fit our method to our synthetic images, we use real telescope images and attempt to recover the transformation parameters.en_US
dc.description.sponsorshipThe material is based upon work supported by NASA under award number 80GSFC21M0002 and by a UMBC Undergraduate Research Award.en_US
dc.description.urihttps://link.springer.com/chapter/10.1007/978-3-031-25056-9_6en_US
dc.format.extent16 pagesen_US
dc.genrebook chaptersen_US
dc.genreconference papers and proceedingsen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2vkbi-o6rj
dc.identifier.citationSchuckman, M., Prouty, R., Chapman, D., Engel, D. (2023). Using Moffat Profiles to Register Astronomical Images. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13801. Springer, Cham. https://doi.org/10.1007/978-3-031-25056-9_6en_US
dc.identifier.isbn978-3-031-25056-9
dc.identifier.urihttps://doi.org/10.1007/978-3-031-25056-9_6
dc.identifier.urihttp://hdl.handle.net/11603/28224
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Imaging Research Center (IRC)
dc.relation.ispartofUMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
dc.relation.ispartofUMBC Office for the Vice President of Research & Creative Achievement (ORCA)
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAccess to this item will begin on 2/15/2025.
dc.titleUsing Moffat Profiles to Register Astronomical Imagesen_US
dc.title.alternativeUsing Moffat Profiles to Register Astronomical Images for Super-resolution
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-1486-4796en_US
dcterms.creatorhttps://orcid.org/0000-0001-9223-9147en_US
dcterms.creatorhttps://orcid.org/0000-0003-1722-9883en_US
dcterms.creatorhttps://orcid.org/0000-0003-2838-0140en_US

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