Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units
The computer vision-based roadside occupation surveillance system is a key infrastructure component of Cooperative Intelligent Transport Systems. However, traffic images captured under low-light conditions suffer from low visibility and unexpected noise. Despite the great progress achieved in recent...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10163809/ |
| _version_ | 1797788508504457216 |
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| author | Chen Yaqing Wang Huaming |
| author_facet | Chen Yaqing Wang Huaming |
| author_sort | Chen Yaqing |
| collection | DOAJ |
| description | The computer vision-based roadside occupation surveillance system is a key infrastructure component of Cooperative Intelligent Transport Systems. However, traffic images captured under low-light conditions suffer from low visibility and unexpected noise. Despite the great progress achieved in recent years, the existing night image enhancement algorithms often suffer from color deviation, ghosting, and overexposure problems in practical traffic applications. Thus, we present a novel night color image enhancement approach to overcome this issue by combining multi-sensor fusion and pseudo-multi-exposure fusion techniques. Unlike the traditional exposure adjustment-based approaches, we performed a novel bidirectional region segmentation-based inverse tone mapping operator to generate pseudo-multi-exposure sequences from day and night image pairs. Meanwhile, to solve the problem that moving objects are diluted after fusion, a partial differential equation (PDE)-based luminance stretching is applied to the moving areas to guarantee that the enhanced image always highlights the traffic targets. Instead of image feature-based methods for moving object detection, we generate more accurate moving regions by fusing data from the radar and camera sensors. Finally, a pyramid-based fusion method with an improved weight function is conducted to generate high-quality traffic images. The proposed method and five state-of-the-art methods are evaluated on randomly selected images from the Rope-3D database and nighttime images captured by an Intelligent Roadside Surveillance System. The experimental results demonstrate that our method has significant advantages in enhancing details and making colors more natural for human observation. |
| first_indexed | 2024-03-13T01:37:34Z |
| format | Article |
| id | doaj.art-247ab93e99e2487da2801f28c52caaa6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T01:37:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-247ab93e99e2487da2801f28c52caaa62023-07-03T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111644946450610.1109/ACCESS.2023.328959510163809Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside UnitsChen Yaqing0https://orcid.org/0000-0002-8717-506XWang Huaming1https://orcid.org/0000-0002-4434-7482College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, ChinaThe computer vision-based roadside occupation surveillance system is a key infrastructure component of Cooperative Intelligent Transport Systems. However, traffic images captured under low-light conditions suffer from low visibility and unexpected noise. Despite the great progress achieved in recent years, the existing night image enhancement algorithms often suffer from color deviation, ghosting, and overexposure problems in practical traffic applications. Thus, we present a novel night color image enhancement approach to overcome this issue by combining multi-sensor fusion and pseudo-multi-exposure fusion techniques. Unlike the traditional exposure adjustment-based approaches, we performed a novel bidirectional region segmentation-based inverse tone mapping operator to generate pseudo-multi-exposure sequences from day and night image pairs. Meanwhile, to solve the problem that moving objects are diluted after fusion, a partial differential equation (PDE)-based luminance stretching is applied to the moving areas to guarantee that the enhanced image always highlights the traffic targets. Instead of image feature-based methods for moving object detection, we generate more accurate moving regions by fusing data from the radar and camera sensors. Finally, a pyramid-based fusion method with an improved weight function is conducted to generate high-quality traffic images. The proposed method and five state-of-the-art methods are evaluated on randomly selected images from the Rope-3D database and nighttime images captured by an Intelligent Roadside Surveillance System. The experimental results demonstrate that our method has significant advantages in enhancing details and making colors more natural for human observation.https://ieeexplore.ieee.org/document/10163809/Intelligent transportroadside unitsimage enhancementmulti-exposure fusion |
| spellingShingle | Chen Yaqing Wang Huaming Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units IEEE Access Intelligent transport roadside units image enhancement multi-exposure fusion |
| title | Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units |
| title_full | Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units |
| title_fullStr | Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units |
| title_full_unstemmed | Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units |
| title_short | Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units |
| title_sort | multi exposure fusion with guidance information night color image enhancement for roadside units |
| topic | Intelligent transport roadside units image enhancement multi-exposure fusion |
| url | https://ieeexplore.ieee.org/document/10163809/ |
| work_keys_str_mv | AT chenyaqing multiexposurefusionwithguidanceinformationnightcolorimageenhancementforroadsideunits AT wanghuaming multiexposurefusionwithguidanceinformationnightcolorimageenhancementforroadsideunits |