Enhancing Low‐Light Images: A Variation‐based Retinex with Modified Bilateral Total Variation and Tensor Sparse Coding
Low‐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...
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| Published in: | Computer graphics forum Vol. 42; no. 7 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.10.2023
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Low‐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‐of‐the‐art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex. |
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| AbstractList | Low‐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‐of‐the‐art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex. Low‐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‐of‐the‐art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex . |
| Author | Gao, Hongxia Zou, Wenbin Ma, Jianliang Chen, Hongsheng Yang, Weipeng Huang, Shasha |
| Author_xml | – sequence: 1 givenname: Weipeng orcidid: 0009-0004-0971-4621 surname: Yang fullname: Yang, Weipeng organization: South China University of Technology – sequence: 2 givenname: Hongxia orcidid: 0000-0003-4166-6864 surname: Gao fullname: Gao, Hongxia email: hxgao@scut.edu.cn organization: Pazhou Laboratory – sequence: 3 givenname: Wenbin orcidid: 0000-0002-7156-0115 surname: Zou fullname: Zou, Wenbin organization: South China University of Technology – sequence: 4 givenname: Shasha orcidid: 0009-0002-3433-7800 surname: Huang fullname: Huang, Shasha organization: South China University of Technology – sequence: 5 givenname: Hongsheng orcidid: 0009-0009-5334-4492 surname: Chen fullname: Chen, Hongsheng organization: South China University of Technology – sequence: 6 givenname: Jianliang orcidid: 0009-0000-4445-6294 surname: Ma fullname: Ma, Jianliang organization: KUKA Robotics Guangdong Co., Ltd |
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| Snippet | Low‐light conditions often result in the presence of significant noise and artifacts in captured images, which can be further exacerbated during the image... |
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| SubjectTerms | CCS Concepts Coding Computing methodologies → Image processing Datasets Illuminance Image enhancement Low‐level‐vision tasks Mathematical analysis Regularization Tensors |
| Title | Enhancing Low‐Light Images: A Variation‐based Retinex with Modified Bilateral Total Variation and Tensor Sparse Coding |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcgf.14960 https://www.proquest.com/docview/2898259450 |
| Volume | 42 |
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