Region-Adaptive Low-Light Image Enhancement with Light Effect Suppression and Detail Preservation

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dc.contributor.authorLuo, Lihengen_US
dc.contributor.authorXie, Wantongen_US
dc.contributor.authorXia, Xiushanen_US
dc.contributor.authorLi, Zeruien_US
dc.contributor.authorZhao, Yunboen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:03:29Z
dc.date.available2025-10-07T06:03:29Z
dc.date.issued2025
dc.description.abstractLow-light image enhancement seeks to improve the visual quality of images captured under poor illumination, yet existing methods often struggle with unnatural artifacts, overexposure, or detail loss, particularly in challenging real-world scenarios like underground coal mines. We propose a novel unsupervised region-adaptive framework that integrates light effect suppression and detail preservation to address these issues. Leveraging Retinex theory, our approach decomposes images into illumination and reflectance components, employing a region segmentation module to distinguish dark and bright areas for targeted enhancement. A lightweight denoising network mitigates noise, while an adaptive illumination enhancer and light effect suppressor collaboratively optimize illumination to ensure natural appearance and correct visual imbalances. A composite loss function balances brightness enhancement, structural integrity, and artifact suppression across regions. Extensive experiments on the LOL-v2, LSRW and our private datasets demonstrate superior performance. For instance, on our dataset, improvements of 3.26% in BRISQUE, 0.24% in NIQE, and 11.22% in PIQE were achieved compared to state-of-the-art methods, providing visually pleasing results with enhanced brightness, reduced artifacts, and preserved textures, making it well-suited for real-world applications.en_US
dc.description.sectionheadersEnhancing Images
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251274
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251274
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251274
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Image processing; Artificial intelligence
dc.subjectComputing methodologies → Image processing
dc.subjectArtificial intelligence
dc.titleRegion-Adaptive Low-Light Image Enhancement with Light Effect Suppression and Detail Preservationen_US
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