Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorBarzel, Shir
dc.contributor.authorSalhov, Moshe
dc.contributor.authorLindenbaum, Ofir
dc.contributor.authorAverbuch, Amir
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-24T19:16:58Z-
dc.date.available2024-07-24T19:16:58Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 13-22.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57373
dc.format10 scs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencycs_CZ
dc.subjectbarevný prostor CIE-XYZcs
dc.subjectsRGBcs
dc.subjectrekonstrukce obrazucs
dc.subjectsamokontrolované učenícs
dc.subjectnezpracovaný obrazcs
dc.subjectMacbeth ColorCheckercs
dc.titleSEL-CIE: Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Imagesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedModern cameras typically offer two types of image states: a minimally processed linear raw RGB image repre senting the raw sensor data, and a highly-processed non-linear image state, such as the sRGB state. The CIE-XYZ color space is a device-independent linear space used as part of the camera pipeline and can be helpful for com puter vision tasks, such as image deblurring, dehazing, and color recognition tasks in medical applications, where color accuracy is important. However, images are usually saved in non-linear states, and achieving CIE-XYZ color images using conventional methods is not always possible. To tackle this issue, classical methodologies have been developed that focus on reversing the acquisition pipeline. More recently, supervised learning has been employed, using paired CIE-XYZ and sRGB representations of identical images. However, obtaining a large-scale dataset of CIE-XYZ and sRGB pairs can be challenging. To overcome this limitation and mitigate the reliance on large amounts of paired data, self-supervised learning (SSL) can be utilized as a substitute for relying solely on paired data. This paper proposes a framework for using SSL methods alongside paired data to reconstruct CIE-XYZ images and re-render sRGB images, outperforming existing approaches. The proposed framework is applied to the sRGB2XYZ dataseten
dc.subject.translatedCIE-XYZ Color Spaceen
dc.subject.translatedsRGBen
dc.subject.translatedimage reconstructionen
dc.subject.translatedSelf-Supervised Learningen
dc.subject.translatedraw imageen
dc.subject.translatedMacbeth ColorCheckeren
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.2
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:WSCG 2024: Full Papers Proceedings

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
A13-2024.pdfPlný text6,35 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/57373

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Full Papers
  5. WSCG 2024: Full Papers Proceedings