Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework
Images captured under low-light conditions often suffer from serious degradation due to insufficient light, which adversely impacts subsequent computer vision tasks. Retinex-based methods have demonstrated strong potential in low-light image enhancement. However, existing approaches often directly d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/24/4025 |
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| author | Chenping Zhao Wenlong Yue Yingjun Wang Jianping Wang Shousheng Luo Huazhu Chen Yan Wang |
| author_facet | Chenping Zhao Wenlong Yue Yingjun Wang Jianping Wang Shousheng Luo Huazhu Chen Yan Wang |
| author_sort | Chenping Zhao |
| collection | DOAJ |
| description | Images captured under low-light conditions often suffer from serious degradation due to insufficient light, which adversely impacts subsequent computer vision tasks. Retinex-based methods have demonstrated strong potential in low-light image enhancement. However, existing approaches often directly design prior regularization functions for either illumination or reflectance components, which may unintentionally introduce noise. To address these limitations, this paper presents an enhancement method by integrating a Plug-and-Play strategy into an extended decomposition model. The proposed model consists of three main components: an extended decomposition term, an iterative reweighting regularization function for the illumination component, and a Plug-and-Play refinement term applied to the reflectance component. The extended decomposition enables a more precise representation of image components, while the iterative reweighting mechanism allows for gentle smoothing near edges and brighter areas while applying more pronounced smoothing in darker regions. Additionally, the Plug-and-Play framework incorporates off-the-shelf image denoising filters to effectively suppress noise and preserve useful image details. Extensive experiments on several datasets confirm that the proposed method consistently outperforms existing techniques. |
| format | Article |
| id | doaj-art-bc8488a69ffe4b1b846b2a048e30db51 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-bc8488a69ffe4b1b846b2a048e30db512025-08-20T02:00:34ZengMDPI AGMathematics2227-73902024-12-011224402510.3390/math12244025Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play FrameworkChenping Zhao0Wenlong Yue1Yingjun Wang2Jianping Wang3Shousheng Luo4Huazhu Chen5Yan Wang6Postdoctoral Research Station of Physics, Henan Normal University, Xinxiang 453007, ChinaSchool of Mathematical Science, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321000, ChinaSchool of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou 451191, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaImages captured under low-light conditions often suffer from serious degradation due to insufficient light, which adversely impacts subsequent computer vision tasks. Retinex-based methods have demonstrated strong potential in low-light image enhancement. However, existing approaches often directly design prior regularization functions for either illumination or reflectance components, which may unintentionally introduce noise. To address these limitations, this paper presents an enhancement method by integrating a Plug-and-Play strategy into an extended decomposition model. The proposed model consists of three main components: an extended decomposition term, an iterative reweighting regularization function for the illumination component, and a Plug-and-Play refinement term applied to the reflectance component. The extended decomposition enables a more precise representation of image components, while the iterative reweighting mechanism allows for gentle smoothing near edges and brighter areas while applying more pronounced smoothing in darker regions. Additionally, the Plug-and-Play framework incorporates off-the-shelf image denoising filters to effectively suppress noise and preserve useful image details. Extensive experiments on several datasets confirm that the proposed method consistently outperforms existing techniques.https://www.mdpi.com/2227-7390/12/24/4025low-light imagedecomposition modeliterative reweighting regularizationPlug-and-Play |
| spellingShingle | Chenping Zhao Wenlong Yue Yingjun Wang Jianping Wang Shousheng Luo Huazhu Chen Yan Wang Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework Mathematics low-light image decomposition model iterative reweighting regularization Plug-and-Play |
| title | Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework |
| title_full | Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework |
| title_fullStr | Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework |
| title_full_unstemmed | Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework |
| title_short | Low-Light Image Enhancement Integrating Retinex-Inspired Extended Decomposition with a Plug-and-Play Framework |
| title_sort | low light image enhancement integrating retinex inspired extended decomposition with a plug and play framework |
| topic | low-light image decomposition model iterative reweighting regularization Plug-and-Play |
| url | https://www.mdpi.com/2227-7390/12/24/4025 |
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