A TransISP Based Image Enhancement Method for Visual Disbalance in Low‐light Images
Existing image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low‐light images. To overcome these challenges, we propose a TransISP‐based image enhancement method specifically designed for low‐light images....
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| Vydáno v: | Computer graphics forum Ročník 43; číslo 7 |
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01.10.2024
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Existing image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low‐light images. To overcome these challenges, we propose a TransISP‐based image enhancement method specifically designed for low‐light images. To mitigate color distortion, we design dual encoders based on decoupled representation learning, which enable complete decoupling of the reflection and illumination components, thereby preventing mutual interference during the image enhancement process. To address brightness unevenness, we introduce CNNformer, a hybrid model combining CNN and Transformer. This model efficiently captures local details and long‐distance dependencies between pixels, contributing to the enhancement of brightness features across various local regions. Additionally, we integrate traditional image signal processing algorithms to achieve efficient color correction and denoising of the reflection component. Furthermore, we employ a generative adversarial network (GAN) as the overarching framework to facilitate unsupervised learning. The experimental results show that, compared with six SOTA image enhancement algorithms, our method obtains significant improvement in evaluation indexes (e.g., on LOL, PSNR: 15.59%, SSIM: 9.77%, VIF: 9.65%), and it can improve visual disbalance defects in low‐light images captured from real‐world coal mine underground scenarios. |
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| AbstractList | Existing image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low‐light images. To overcome these challenges, we propose a TransISP‐based image enhancement method specifically designed for low‐light images. To mitigate color distortion, we design dual encoders based on decoupled representation learning, which enable complete decoupling of the reflection and illumination components, thereby preventing mutual interference during the image enhancement process. To address brightness unevenness, we introduce CNNformer, a hybrid model combining CNN and Transformer. This model efficiently captures local details and long‐distance dependencies between pixels, contributing to the enhancement of brightness features across various local regions. Additionally, we integrate traditional image signal processing algorithms to achieve efficient color correction and denoising of the reflection component. Furthermore, we employ a generative adversarial network (GAN) as the overarching framework to facilitate unsupervised learning. The experimental results show that, compared with six SOTA image enhancement algorithms, our method obtains significant improvement in evaluation indexes (e.g., on LOL, PSNR: 15.59%, SSIM: 9.77%, VIF: 9.65%), and it can improve visual disbalance defects in low‐light images captured from real‐world coal mine underground scenarios. |
| Author | Zhang, Shihao Chen, Wei Wu, Jiaqi Jing, Rui Tian, Zijian Wang, Zehua Guo, Jing |
| Author_xml | – sequence: 1 givenname: Jiaqi orcidid: 0000-0002-1587-163X surname: Wu fullname: Wu, Jiaqi organization: University of British Columbia – sequence: 2 givenname: Jing surname: Guo fullname: Guo, Jing organization: China University of Mining & Technology – sequence: 3 givenname: Rui surname: Jing fullname: Jing, Rui organization: China University of Mining & Technology – sequence: 4 givenname: Shihao surname: Zhang fullname: Zhang, Shihao organization: China University of Mining & Technology – sequence: 5 givenname: Zijian orcidid: 0000-0002-4389-8821 surname: Tian fullname: Tian, Zijian organization: China University of Mining & Technology – sequence: 6 givenname: Wei orcidid: 0000-0002-7663-278X surname: Chen fullname: Chen, Wei email: chenwdavior@163.com organization: China University of Mining and Technology – sequence: 7 givenname: Zehua orcidid: 0000-0001-9040-847X surname: Wang fullname: Wang, Zehua organization: University of British Columbia |
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| Cites_doi | 10.1109/TIP.2007.901238 10.1109/ACCESS.2023.3286538 10.1109/CVPR.2011.5995413 10.1109/INFOCT.2019.8710893 10.1109/TIP.2021.3051462 10.1016/j.patcog.2016.06.008 10.1038/s42256-020-00270-2 10.3390/app13042672 10.1109/ACSSC.2003.1292216 10.1007/978-3-319-46475-6_43 10.1109/TPAMI.2021.3126387 10.1109/83.557356 10.1117/1.JEI.29.3.033012 10.1016/j.eswa.2021.115034 10.1155/2021/5578289 10.1109/ICCV51070.2023.00743 10.1109/JSEN.2020.2981719 10.1109/TPAMI.2022.3164083 10.1109/CVPR42600.2020.00185 10.1109/TBC.2019.2960942 10.1109/CVPR.2018.00165 10.1109/ICIP.2015.7351031 10.1109/CVPR52688.2022.00581 10.1145/3422622 |
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| SubjectTerms | Algorithms Brightness CCS Concepts Coal mines Color Computing methodologies → Computer vision problems Decoupling Distortion Generative adversarial networks Image enhancement Image processing Light Light reflection Machine learning Signal reflection Underground mines Unevenness Unsupervised learning Visibility |
| Title | A TransISP Based Image Enhancement Method for Visual Disbalance in Low‐light Images |
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