Nighttime image semantic segmentation with retinex theory
Nighttime image semantic segmentation is challenging due to low-light and diverse lighting conditions. A straightforward solution is to first enhance nighttime scene images to resemble daytime scene before performing segmentation. This kind of methods heavily rely on the enhancement quality. Inspire...
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| Vydáno v: | Image and vision computing Ročník 148; s. 105149 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.08.2024
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| Témata: | |
| ISSN: | 0262-8856, 1872-8138 |
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| Abstract | Nighttime image semantic segmentation is challenging due to low-light and diverse lighting conditions. A straightforward solution is to first enhance nighttime scene images to resemble daytime scene before performing segmentation. This kind of methods heavily rely on the enhancement quality. Inspired by the Retinex theory for low-light image enhancement, which decomposes an image into reflectance and illumination components, we propose a novel nighttime image segmentation method with Retinex theory (RNightSeg). Our core insight is to obtain high-quality illumination-independent reflectance component to enhance segmentation. Specifically, we apply a decomposition decoder to the backbone network for generating the reflectance component. In addition to the fidelity loss and Total Variation loss for the reflectance component regression, we model the brightening illumination component to enhance the nighttime image and apply the color constancy loss on the enhanced image. This helps to cope with the issue of low-light and diverse lighting in the nighttime scene. Finally, we fuse the reflectance decoder feature with the backbone feature and feed the fused feature to the segmentation decoder. Extensive experimental results on two widely used datasets demonstrate that the proposed RNightSeg achieves superior performance over some state-of-the-art segmentation methods. The code of our implementation is available at https://github.com/sunzc-sunny/RNightSeg.
•Developed a Retinex-based network RNightSeg to enhance nighttime image segmentation.•Introduced illumination-independent reflectance component for accurate segmentation.•Method improves performance across diverse nighttime illumination conditions. |
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| AbstractList | Nighttime image semantic segmentation is challenging due to low-light and diverse lighting conditions. A straightforward solution is to first enhance nighttime scene images to resemble daytime scene before performing segmentation. This kind of methods heavily rely on the enhancement quality. Inspired by the Retinex theory for low-light image enhancement, which decomposes an image into reflectance and illumination components, we propose a novel nighttime image segmentation method with Retinex theory (RNightSeg). Our core insight is to obtain high-quality illumination-independent reflectance component to enhance segmentation. Specifically, we apply a decomposition decoder to the backbone network for generating the reflectance component. In addition to the fidelity loss and Total Variation loss for the reflectance component regression, we model the brightening illumination component to enhance the nighttime image and apply the color constancy loss on the enhanced image. This helps to cope with the issue of low-light and diverse lighting in the nighttime scene. Finally, we fuse the reflectance decoder feature with the backbone feature and feed the fused feature to the segmentation decoder. Extensive experimental results on two widely used datasets demonstrate that the proposed RNightSeg achieves superior performance over some state-of-the-art segmentation methods. The code of our implementation is available at https://github.com/sunzc-sunny/RNightSeg.
•Developed a Retinex-based network RNightSeg to enhance nighttime image segmentation.•Introduced illumination-independent reflectance component for accurate segmentation.•Method improves performance across diverse nighttime illumination conditions. |
| ArticleNumber | 105149 |
| Author | Xiao, Xin Sun, Zhichao Gu, Yuliang Xu, Yongchao Zhu, Huachao |
| Author_xml | – sequence: 1 givenname: Zhichao surname: Sun fullname: Sun, Zhichao – sequence: 2 givenname: Huachao surname: Zhu fullname: Zhu, Huachao – sequence: 3 givenname: Xin surname: Xiao fullname: Xiao, Xin – sequence: 4 givenname: Yuliang surname: Gu fullname: Gu, Yuliang – sequence: 5 givenname: Yongchao surname: Xu fullname: Xu, Yongchao email: yongchao.xu@whu.edu.cn |
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| Cites_doi | 10.1016/j.imavis.2022.104470 10.1109/TIP.2019.2958144 10.1109/TNNLS.2020.3028503 10.1007/s11704-015-4353-1 10.1016/j.imavis.2022.104554 10.1007/s11042-019-08404-4 10.1109/TIP.2021.3122004 10.1109/TIP.2021.3051462 10.1109/TITS.2020.3033569 10.1109/TPAMI.2021.3138829 10.1109/TPAMI.2020.3045882 10.1109/TIP.2021.3050850 10.1109/TITS.2020.3042973 10.1109/TPAMI.2017.2699184 10.1109/83.557356 10.1109/TITS.2022.3177615 10.1016/0016-0032(80)90058-7 10.1007/s11704-018-7195-8 10.1016/j.imavis.2023.104834 10.1109/TIP.2022.3196546 10.1016/j.imavis.2024.104933 10.1109/TIP.2015.2474701 10.1109/TCSVT.2021.3073371 10.1109/TIP.2013.2261309 10.1109/TIP.2018.2810539 10.1109/TITS.2020.2972912 10.1109/TPAMI.2023.3246102 10.1038/scientificamerican1277-108 10.1109/TPAMI.2022.3217046 10.1016/j.engappai.2021.104171 |
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| Copyright | 2024 Elsevier B.V. |
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| Keywords | Semantic segmentation Retinex decomposition Nighttime vision |
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