Reversing image signal processors by reverse style transferring
dc.contributor.author | Kınlı, Osman Furkan | |
dc.contributor.author | Özcan, Barış | |
dc.contributor.author | Kıraç, Mustafa Furkan | |
dc.date.accessioned | 2024-01-25T06:38:11Z | |
dc.date.available | 2024-01-25T06:38:11Z | |
dc.date.issued | 2023 | |
dc.identifier.isbn | 978-303125062-0 | |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/9097 | |
dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-031-25063-7_43 | |
dc.description.abstract | RAW image datasets are more suitable than the standard RGB image datasets for the ill-posed inverse problems in low-level vision, but not common in the literature. There are also a few studies to focus on mapping sRGB images to RAW format. Mapping from sRGB to RAW format could be a relevant domain for reverse style transferring since the task is an ill-posed reversing problem. In this study, we seek an answer to the question: Can the ISP operations be modeled as the style factor in an end-to-end learning pipeline? To investigate this idea, we propose a novel architecture, namely RST-ISP-Net, for learning to reverse the ISP operations with the help of adaptive feature normalization. We formulate this problem as a reverse style transferring and mostly follow the practice used in the prior work. We have participated in the AIM Reversed ISP challenge with our proposed architecture. Results indicate that the idea of modeling disruptive or modifying factors as style is still valid, but further improvements are required to be competitive in such a challenge. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | ECCV 2022: Computer Vision – ECCV 2022 Workshops, Part of the Lecture Notes in Computer Science book series (LNCS,volume 13802) | |
dc.rights | restrictedAccess | |
dc.title | Reversing image signal processors by reverse style transferring | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-9192-6583 & YÖK ID 283852) Kınlı, Furkan | |
dc.contributor.authorID | (ORCID 0000-0001-9177-0489 & YÖK ID 124619) Kıraç, Furkan | |
dc.contributor.ozuauthor | Kınlı, Osman Furkan | |
dc.contributor.ozuauthor | Kıraç, Mustafa Furkan | |
dc.identifier.volume | 13802 LNCS | en_US |
dc.identifier.startpage | 688 | en_US |
dc.identifier.endpage | 698 | en_US |
dc.identifier.doi | 10.1007/978-3-031-25063-7_43 | en_US |
dc.subject.keywords | Image signal processors | en_US |
dc.subject.keywords | Reverse style transfer | en_US |
dc.subject.keywords | sRGB-to-RAW reconstruction | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85151046384 | |
dc.contributor.ozugradstudent | Özcan, Barış | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff and PhD Student |
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