A dual-guide aware normalizing flow for low-light enhancement with global illumination and cross-channel attention.
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| Title: | A dual-guide aware normalizing flow for low-light enhancement with global illumination and cross-channel attention. |
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| Authors: | Zhuang, Xue1 (AUTHOR) 2231221075@stu.xaut.edu.cn, Xiao, Zhaolin1 (AUTHOR) xiaozhaolin@xaut.edu.cn, Su, Haonan1 (AUTHOR) suhaonan@xaut.edu.cn |
| Source: | Multimedia Systems. Jun2026, Vol. 32 Issue 4, p1-16. 16p. |
| Subjects: | Image enhancement (Imaging systems), Luminosity, Night photography, Machine learning, Probabilistic generative models, Computer vision, Image processing |
| Abstract: | Low Light Image Enhancement (LLIE) focuses on improving the quality of images captured under insufficient lighting conditions. Recently, normalizing flow model has been introduced to LLIE task due to its powerful capability in modeling data distribution. However, existing flow-based methods often overlook the importance of conditional features in guiding accurate probabilistic modeling, which can lead to uneven illumination and unnatural color restoration. To address this issue, we propose a Dual-Guide aware Normalizing Flow (DGNF) framework, which enhances encoding quality and injects richer conditional features into the flow-based decoder. Specifically, the proposed Dual-Guided aware Encoder Module (DGEM) consists of two specialized components–the Illumination-Guided Encoder Module (IGEM) and the Color-Guided Encoder Module (CGEM), which accurate models the features of illuminance and color of low light images respectively. The Illumination and Color aware Normalizing Flow Decoder (ICNFD) is designed to inject encoded features into the flow-based decoder, using them as conditional inputs to enable high-quality image enhancement. Experimental results on the LOL-V1 and LOL-V2 datasets show that our method achieves improvements of 1.36 dB in PSNR and 0.005 in SSIM compared to state-of-the-art methods, demonstrating the effectiveness of the proposed encoder in content preservation, noise reduction, and color restoration. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192203620 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A dual-guide aware normalizing flow for low-light enhancement with global illumination and cross-channel attention. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhuang%2C+Xue%22">Zhuang, Xue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2231221075@stu.xaut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Zhaolin%22">Xiao, Zhaolin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xiaozhaolin@xaut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Su%2C+Haonan%22">Su, Haonan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> suhaonan@xaut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Systems%22">Multimedia Systems</searchLink>. Jun2026, Vol. 32 Issue 4, p1-16. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Luminosity%22">Luminosity</searchLink><br /><searchLink fieldCode="DE" term="%22Night+photography%22">Night photography</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Low Light Image Enhancement (LLIE) focuses on improving the quality of images captured under insufficient lighting conditions. Recently, normalizing flow model has been introduced to LLIE task due to its powerful capability in modeling data distribution. However, existing flow-based methods often overlook the importance of conditional features in guiding accurate probabilistic modeling, which can lead to uneven illumination and unnatural color restoration. To address this issue, we propose a Dual-Guide aware Normalizing Flow (DGNF) framework, which enhances encoding quality and injects richer conditional features into the flow-based decoder. Specifically, the proposed Dual-Guided aware Encoder Module (DGEM) consists of two specialized components–the Illumination-Guided Encoder Module (IGEM) and the Color-Guided Encoder Module (CGEM), which accurate models the features of illuminance and color of low light images respectively. The Illumination and Color aware Normalizing Flow Decoder (ICNFD) is designed to inject encoded features into the flow-based decoder, using them as conditional inputs to enable high-quality image enhancement. Experimental results on the LOL-V1 and LOL-V2 datasets show that our method achieves improvements of 1.36 dB in PSNR and 0.005 in SSIM compared to state-of-the-art methods, demonstrating the effectiveness of the proposed encoder in content preservation, noise reduction, and color restoration. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00530-026-02241-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Image enhancement (Imaging systems) Type: general – SubjectFull: Luminosity Type: general – SubjectFull: Night photography Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: A dual-guide aware normalizing flow for low-light enhancement with global illumination and cross-channel attention. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhuang, Xue – PersonEntity: Name: NameFull: Xiao, Zhaolin – PersonEntity: Name: NameFull: Su, Haonan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09424962 Numbering: – Type: volume Value: 32 – Type: issue Value: 4 Titles: – TitleFull: Multimedia Systems Type: main |
| ResultId | 1 |
