Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems
Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6658763 |
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| _version_ | 1832568532833927168 |
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| author | Xianjun Hu Jing Wang Chunlei Zhang Yishuo Tong |
| author_facet | Xianjun Hu Jing Wang Chunlei Zhang Yishuo Tong |
| author_sort | Xianjun Hu |
| collection | DOAJ |
| description | Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking. |
| format | Article |
| id | doaj-art-e29140b8e85f4a1ba072c49317c5fb4b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e29140b8e85f4a1ba072c49317c5fb4b2025-02-03T00:58:56ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66587636658763Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation SystemsXianjun Hu0Jing Wang1Chunlei Zhang2Yishuo Tong3College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, ChinaChina Aerospace Science and Industry Corporation, Beijing 100048, ChinaCollege of Electronic Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Electronic Engineering, Naval University of Engineering, Wuhan 430033, ChinaImage dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.http://dx.doi.org/10.1155/2021/6658763 |
| spellingShingle | Xianjun Hu Jing Wang Chunlei Zhang Yishuo Tong Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems Journal of Advanced Transportation |
| title | Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems |
| title_full | Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems |
| title_fullStr | Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems |
| title_full_unstemmed | Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems |
| title_short | Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems |
| title_sort | deep learning enabled variational optimization method for image dehazing in maritime intelligent transportation systems |
| url | http://dx.doi.org/10.1155/2021/6658763 |
| work_keys_str_mv | AT xianjunhu deeplearningenabledvariationaloptimizationmethodforimagedehazinginmaritimeintelligenttransportationsystems AT jingwang deeplearningenabledvariationaloptimizationmethodforimagedehazinginmaritimeintelligenttransportationsystems AT chunleizhang deeplearningenabledvariationaloptimizationmethodforimagedehazinginmaritimeintelligenttransportationsystems AT yishuotong deeplearningenabledvariationaloptimizationmethodforimagedehazinginmaritimeintelligenttransportationsystems |