Enhancing Low-Light Images: A Variation-based Retinex with Modified Bilateral Total Variation and Tensor Sparse Coding

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dc.contributor.authorYang, Weipengen_US
dc.contributor.authorGao, Hongxiaen_US
dc.contributor.authorZou, Wenbinen_US
dc.contributor.authorHuang, Shashaen_US
dc.contributor.authorChen, Hongshengen_US
dc.contributor.authorMa, Jianliangen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:35:25Z
dc.date.available2023-10-09T07:35:25Z
dc.date.issued2023
dc.description.abstractLow-light conditions often result in the presence of significant noise and artifacts in captured images, which can be further exacerbated during the image enhancement process, leading to a decrease in visual quality. This paper aims to present an effective low-light image enhancement model based on the variation Retinex model that successfully suppresses noise and artifacts while preserving image details. To achieve this, we propose a modified Bilateral Total Variation to better smooth out fine textures in the illuminance component while maintaining weak structures. Additionally, tensor sparse coding is employed as a regularization term to remove noise and artifacts from the reflectance component. Experimental results on extensive and challenging datasets demonstrate the effectiveness of the proposed method, exhibiting superior or comparable performance compared to state-ofthe- art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex.en_US
dc.description.number7
dc.description.sectionheadersImaging
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14960
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14960
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14960
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Image processing; Low-level-vision tasks
dc.subjectComputing methodologies
dc.subjectImage processing
dc.subjectLow
dc.subjectlevel
dc.subjectvision tasks
dc.titleEnhancing Low-Light Images: A Variation-based Retinex with Modified Bilateral Total Variation and Tensor Sparse Codingen_US
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