Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model
It is well known that images taken in low-light conditions frequently suffer from unknown noise and low visibility, which can pose challenges for image enhancement. The majority of Retinex-based decomposition algorithms usually attempt to directly design prior regularization for illumination or refl...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/18/3834 |
| _version_ | 1827725461150498816 |
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| author | Chenping Zhao Wenlong Yue Jianlou Xu Huazhu Chen |
| author_facet | Chenping Zhao Wenlong Yue Jianlou Xu Huazhu Chen |
| author_sort | Chenping Zhao |
| collection | DOAJ |
| description | It is well known that images taken in low-light conditions frequently suffer from unknown noise and low visibility, which can pose challenges for image enhancement. The majority of Retinex-based decomposition algorithms usually attempt to directly design prior regularization for illumination or reflectance. Nevertheless, noise can be involved in such schemes. To address these issues, a new Retinex-based decomposition model for simultaneous enhancement and denoising has been developed. In this paper, an extended decomposition scheme is introduced to extract the illumination and reflectance components, which helps to better describe the prior information on illumination and reflectance. Subsequently, spatially adaptive weights are designed for two regularization terms. The main motivation is to provide a small amount of smoothing in near edges or bright areas and stronger smoothing in dark areas, which could preserve useful information and remove noise effectively during image-enhancement processing. Finally, the proposed algorithm is validated on several common datasets: LIME, LOL, and NPE. Extensive experiments show that the presented method is superior to state-of-the-art methods both in objective index comparisons and visual quality. |
| first_indexed | 2024-03-10T22:29:39Z |
| format | Article |
| id | doaj.art-162dcad6592a44d08a87b7cd7ecc2043 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:29:39Z |
| publishDate | 2023-09-01 |
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| series | Mathematics |
| spelling | doaj.art-162dcad6592a44d08a87b7cd7ecc20432023-11-19T11:48:15ZengMDPI AGMathematics2227-73902023-09-011118383410.3390/math11183834Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition ModelChenping Zhao0Wenlong Yue1Jianlou Xu2Huazhu Chen3School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou 451191, ChinaIt is well known that images taken in low-light conditions frequently suffer from unknown noise and low visibility, which can pose challenges for image enhancement. The majority of Retinex-based decomposition algorithms usually attempt to directly design prior regularization for illumination or reflectance. Nevertheless, noise can be involved in such schemes. To address these issues, a new Retinex-based decomposition model for simultaneous enhancement and denoising has been developed. In this paper, an extended decomposition scheme is introduced to extract the illumination and reflectance components, which helps to better describe the prior information on illumination and reflectance. Subsequently, spatially adaptive weights are designed for two regularization terms. The main motivation is to provide a small amount of smoothing in near edges or bright areas and stronger smoothing in dark areas, which could preserve useful information and remove noise effectively during image-enhancement processing. Finally, the proposed algorithm is validated on several common datasets: LIME, LOL, and NPE. Extensive experiments show that the presented method is superior to state-of-the-art methods both in objective index comparisons and visual quality.https://www.mdpi.com/2227-7390/11/18/3834low lightenhancement and denoisingRetinexdecomposition model |
| spellingShingle | Chenping Zhao Wenlong Yue Jianlou Xu Huazhu Chen Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model Mathematics low light enhancement and denoising Retinex decomposition model |
| title | Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model |
| title_full | Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model |
| title_fullStr | Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model |
| title_full_unstemmed | Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model |
| title_short | Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model |
| title_sort | joint low light image enhancement and denoising via a new retinex based decomposition model |
| topic | low light enhancement and denoising Retinex decomposition model |
| url | https://www.mdpi.com/2227-7390/11/18/3834 |
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