AVINet: adaptive variational iteration network for low light image enhancement
Abstract Images captured in dark environments often face challenges like low contrast and noise. Many enhancement methods based on variational models, set parameters artificially to improve contrast, which often does not adequately address real-world conditions. To address this, we propose an adapti...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00167-3 |
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| _version_ | 1849225821009477632 |
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| author | Tao Chen Feng-Fei Jin Jinqi Cui Xiangyu Man Dongmei Liu |
| author_facet | Tao Chen Feng-Fei Jin Jinqi Cui Xiangyu Man Dongmei Liu |
| author_sort | Tao Chen |
| collection | DOAJ |
| description | Abstract Images captured in dark environments often face challenges like low contrast and noise. Many enhancement methods based on variational models, set parameters artificially to improve contrast, which often does not adequately address real-world conditions. To address this, we propose an adaptive variational model and establish an Adaptive Variational Iteration Network (AVINet). By designing a Parameter Extraction Module, we can adaptively learn the image contrast adjustment parameters within the model. Then, to prevent uneven image brightness, we introduce a smoothness term. We have mathematically proven the existence and uniqueness of the solution of this variational model, and then solve it using the Bregman iteration method. In the Denoising Module, we employ a denoising diffusion probability model to eliminate the noise in the image. Comparative experiments conducted on various datasets demonstrate that that our method achieves better results in restoring illumination and detail compared to other advanced methods. |
| format | Article |
| id | doaj-art-1bfc99dc25b64d95ba04aac8bf1e5020 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-1bfc99dc25b64d95ba04aac8bf1e50202025-08-24T11:53:48ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711610.1007/s44443-025-00167-3AVINet: adaptive variational iteration network for low light image enhancementTao Chen0Feng-Fei Jin1Jinqi Cui2Xiangyu Man3Dongmei Liu4Shandong Normal UniversityShandong Normal UniversityShandong Normal UniversityShandong Normal UniversityShandong Normal UniversityAbstract Images captured in dark environments often face challenges like low contrast and noise. Many enhancement methods based on variational models, set parameters artificially to improve contrast, which often does not adequately address real-world conditions. To address this, we propose an adaptive variational model and establish an Adaptive Variational Iteration Network (AVINet). By designing a Parameter Extraction Module, we can adaptively learn the image contrast adjustment parameters within the model. Then, to prevent uneven image brightness, we introduce a smoothness term. We have mathematically proven the existence and uniqueness of the solution of this variational model, and then solve it using the Bregman iteration method. In the Denoising Module, we employ a denoising diffusion probability model to eliminate the noise in the image. Comparative experiments conducted on various datasets demonstrate that that our method achieves better results in restoring illumination and detail compared to other advanced methods.https://doi.org/10.1007/s44443-025-00167-3Low light image enhancementVariational modelDenoising diffusion probability model |
| spellingShingle | Tao Chen Feng-Fei Jin Jinqi Cui Xiangyu Man Dongmei Liu AVINet: adaptive variational iteration network for low light image enhancement Journal of King Saud University: Computer and Information Sciences Low light image enhancement Variational model Denoising diffusion probability model |
| title | AVINet: adaptive variational iteration network for low light image enhancement |
| title_full | AVINet: adaptive variational iteration network for low light image enhancement |
| title_fullStr | AVINet: adaptive variational iteration network for low light image enhancement |
| title_full_unstemmed | AVINet: adaptive variational iteration network for low light image enhancement |
| title_short | AVINet: adaptive variational iteration network for low light image enhancement |
| title_sort | avinet adaptive variational iteration network for low light image enhancement |
| topic | Low light image enhancement Variational model Denoising diffusion probability model |
| url | https://doi.org/10.1007/s44443-025-00167-3 |
| work_keys_str_mv | AT taochen avinetadaptivevariationaliterationnetworkforlowlightimageenhancement AT fengfeijin avinetadaptivevariationaliterationnetworkforlowlightimageenhancement AT jinqicui avinetadaptivevariationaliterationnetworkforlowlightimageenhancement AT xiangyuman avinetadaptivevariationaliterationnetworkforlowlightimageenhancement AT dongmeiliu avinetadaptivevariationaliterationnetworkforlowlightimageenhancement |