A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net

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Format: masterThesis
Published: 2020
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Online Access:https://eprints.kfupm.edu.sa/id/eprint/143618/1/Yasmin_Yasin_Master_Thesis_202214360_Final.pdf
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dc.creator.*.fl_str_mv unknown
dc.date.*.fl_str_mv 2020
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/143618/1/Yasmin_Yasin_Master_Thesis_202214360_Final.pdf
A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net. Masters thesis, King Fahd University of Petroleum and Minerals.
dc.language.none.fl_str_mv en
dc.relation.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/143618/
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Computer
Research
dc.title.none.fl_str_mv A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
dc.type.none.fl_str_mv Thesis
NonPeerReviewed
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eu_rights_str_mv openAccess
format masterThesis
id KFUPM_efc04f10bb4ab55d686b1539140a2855
identifier_str_mv A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net. Masters thesis, King Fahd University of Petroleum and Minerals.
language_invalid_str_mv en
network_acronym_str KFUPM
network_name_str King Fahd University of Petroleum and Minerals
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publishDate 2020
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spelling A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-NetComputerResearchThesisNonPeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://eprints.kfupm.edu.sa/id/eprint/143618/1/Yasmin_Yasin_Master_Thesis_202214360_Final.pdf A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net. Masters thesis, King Fahd University of Petroleum and Minerals. enhttps://eprints.kfupm.edu.sa/id/eprint/143618/2020info:eu-repo/semantics/openAccessunknownoai::1436182025-07-22T07:07:19Z
spellingShingle A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
unknown
Computer
Research
status_str publishedVersion
title A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
title_full A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
title_fullStr A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
title_full_unstemmed A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
title_short A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
title_sort A Unified Framework for Unsupervised Backlight and Low-light Image Enhancement via CLIP-Guided Prompt Learning and Symmetric Residual U-Net
topic Computer
Research
url https://eprints.kfupm.edu.sa/id/eprint/143618/1/Yasmin_Yasin_Master_Thesis_202214360_Final.pdf