Low-Light Image Enhancement Using Image-to-Frequency Filter Learning
Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning (DL) approaches. Yet most existing techniques adopt an image-to-image learning paradigm where DL model architectures are constrained due to latent image feature recons...
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2022
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| Online Access: | http://hdl.handle.net/10725/16288 https://doi.org/10.1007/978-3-031-06430-2_58 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/chapter/10.1007/978-3-031-06430-2_58 |
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| _version_ | 1864513472550666240 |
|---|---|
| author | Al Sobbahi, Rayan |
| author2 | Tekli, Joe |
| author2_role | author |
| author_facet | Al Sobbahi, Rayan Tekli, Joe |
| author_role | author |
| dc.contributor.none.fl_str_mv | Sclaroff, Stan Distante, Cosimo Leo, Marco |
| dc.creator.none.fl_str_mv | Al Sobbahi, Rayan Tekli, Joe |
| dc.date.none.fl_str_mv | 2022 2022-05-17 2024-11-08T11:37:50Z 2024-11-08T11:37:50Z |
| dc.identifier.none.fl_str_mv | 9783031064302 http://hdl.handle.net/10725/16288 https://doi.org/10.1007/978-3-031-06430-2_58 Al Sobbahi, R., & Tekli, J. (2022, May). Low-light image enhancement using image-to-frequency filter learning. In International Conference on Image Analysis and Processing (pp. 693-705). Cham: Springer International Publishing. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/chapter/10.1007/978-3-031-06430-2_58 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Springer |
| dc.relation.none.fl_str_mv | 13231 |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Image analysis -- Congresses Image processing -- Digital techniques -- Congresses |
| dc.title.none.fl_str_mv | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning Lecture Notes in Computer Science |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning (DL) approaches. Yet most existing techniques adopt an image-to-image learning paradigm where DL model architectures are constrained due to latent image feature reconstruction. In this paper, we propose a new LLI enhancement solution titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning. It is designed independently from custom DL architectures and can be seamlessly coupled with existing feature extractors like ResNet50 and VGG16. We have conducted a large battery of experiments using SICE and Pascal VOC datasets to evaluate LLHFNet’s enhancement quality. Our solution consistently ranks among the best existing image enhancement techniques and is able to robustly handle LLIs and normal-light images (NLIs). |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_1dfe9da70dd5d439180f1779f43b06d3 |
| identifier_str_mv | 9783031064302 Al Sobbahi, R., & Tekli, J. (2022, May). Low-light image enhancement using image-to-frequency filter learning. In International Conference on Image Analysis and Processing (pp. 693-705). Cham: Springer International Publishing. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/16288 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Springer |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Low-Light Image Enhancement Using Image-to-Frequency Filter LearningLecture Notes in Computer ScienceAl Sobbahi, RayanTekli, JoeImage analysis -- CongressesImage processing -- Digital techniques -- CongressesLow-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning (DL) approaches. Yet most existing techniques adopt an image-to-image learning paradigm where DL model architectures are constrained due to latent image feature reconstruction. In this paper, we propose a new LLI enhancement solution titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning. It is designed independently from custom DL architectures and can be seamlessly coupled with existing feature extractors like ResNet50 and VGG16. We have conducted a large battery of experiments using SICE and Pascal VOC datasets to evaluate LLHFNet’s enhancement quality. Our solution consistently ranks among the best existing image enhancement techniques and is able to robustly handle LLIs and normal-light images (NLIs).1 online resource (xxviii, 793 pages) : illustrations.Includes bibliographical references.SpringerSclaroff, StanDistante, CosimoLeo, Marco2024-11-08T11:37:50Z2024-11-08T11:37:50Z20222022-05-17Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9783031064302http://hdl.handle.net/10725/16288https://doi.org/10.1007/978-3-031-06430-2_58Al Sobbahi, R., & Tekli, J. (2022, May). Low-light image enhancement using image-to-frequency filter learning. In International Conference on Image Analysis and Processing (pp. 693-705). Cham: Springer International Publishing.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://link.springer.com/chapter/10.1007/978-3-031-06430-2_58en13231info:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/162882024-11-08T11:37:50Z |
| spellingShingle | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning Al Sobbahi, Rayan Image analysis -- Congresses Image processing -- Digital techniques -- Congresses |
| status_str | publishedVersion |
| title | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning |
| title_full | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning |
| title_fullStr | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning |
| title_full_unstemmed | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning |
| title_short | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning |
| title_sort | Low-Light Image Enhancement Using Image-to-Frequency Filter Learning |
| topic | Image analysis -- Congresses Image processing -- Digital techniques -- Congresses |
| url | http://hdl.handle.net/10725/16288 https://doi.org/10.1007/978-3-031-06430-2_58 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://link.springer.com/chapter/10.1007/978-3-031-06430-2_58 |