Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance
Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/8/1625 |
| _version_ | 1797584222824693760 |
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| author | Wenbo Zhou Bin Li Guoling Luo |
| author_facet | Wenbo Zhou Bin Li Guoling Luo |
| author_sort | Wenbo Zhou |
| collection | DOAJ |
| description | Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance. |
| first_indexed | 2024-03-10T23:50:02Z |
| format | Article |
| id | doaj.art-fb02de1526914bff8b84fe9da79ea57f |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T23:50:02Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-fb02de1526914bff8b84fe9da79ea57f2023-11-19T01:46:47ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-08-01118162510.3390/jmse11081625Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime SurveillanceWenbo Zhou0Bin Li1Guoling Luo2School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Microelectronics, South China University of Technology, Guangzhou 510641, ChinaZhuhai Metamemory Electronic Technology Co., Ltd., Zhuhai 519090, ChinaLow-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance.https://www.mdpi.com/2077-1312/11/8/1625multiple feature fusionconvolutional neural networkattention mechanismlow-visibility image enhancementmaritime surveillance |
| spellingShingle | Wenbo Zhou Bin Li Guoling Luo Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance Journal of Marine Science and Engineering multiple feature fusion convolutional neural network attention mechanism low-visibility image enhancement maritime surveillance |
| title | Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance |
| title_full | Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance |
| title_fullStr | Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance |
| title_full_unstemmed | Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance |
| title_short | Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance |
| title_sort | multi feature fusion guided low visibility image enhancement for maritime surveillance |
| topic | multiple feature fusion convolutional neural network attention mechanism low-visibility image enhancement maritime surveillance |
| url | https://www.mdpi.com/2077-1312/11/8/1625 |
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