DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device
Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Gene...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8297 |
| _version_ | 1797575159067967488 |
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| author | Jin Qian Hui Li Bin Zhang Sen Lin Xiaoshuang Xing |
| author_facet | Jin Qian Hui Li Bin Zhang Sen Lin Xiaoshuang Xing |
| author_sort | Jin Qian |
| collection | DOAJ |
| description | Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods. |
| first_indexed | 2024-03-10T21:34:44Z |
| format | Article |
| id | doaj.art-7cb437112afd40808d623bc12d24d34d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:34:44Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7cb437112afd40808d623bc12d24d34d2023-11-19T15:05:32ZengMDPI AGSensors1424-82202023-10-012319829710.3390/s23198297DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving DeviceJin Qian0Hui Li1Bin Zhang2Sen Lin3Xiaoshuang Xing4College of Information Engineering, Taizhou University, Taizhou 225300, ChinaCollege of Information Engineering, Taizhou University, Taizhou 225300, ChinaCollege of Information Engineering, Taizhou University, Taizhou 225300, ChinaSchool of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215506, ChinaUnderwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods.https://www.mdpi.com/1424-8220/23/19/8297deep learningunderwater autonomous driving deviceunderwater image enhancementgenerative adversarial network |
| spellingShingle | Jin Qian Hui Li Bin Zhang Sen Lin Xiaoshuang Xing DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device Sensors deep learning underwater autonomous driving device underwater image enhancement generative adversarial network |
| title | DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device |
| title_full | DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device |
| title_fullStr | DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device |
| title_full_unstemmed | DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device |
| title_short | DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device |
| title_sort | drgan dense residual generative adversarial network for image enhancement in an underwater autonomous driving device |
| topic | deep learning underwater autonomous driving device underwater image enhancement generative adversarial network |
| url | https://www.mdpi.com/1424-8220/23/19/8297 |
| work_keys_str_mv | AT jinqian drgandenseresidualgenerativeadversarialnetworkforimageenhancementinanunderwaterautonomousdrivingdevice AT huili drgandenseresidualgenerativeadversarialnetworkforimageenhancementinanunderwaterautonomousdrivingdevice AT binzhang drgandenseresidualgenerativeadversarialnetworkforimageenhancementinanunderwaterautonomousdrivingdevice AT senlin drgandenseresidualgenerativeadversarialnetworkforimageenhancementinanunderwaterautonomousdrivingdevice AT xiaoshuangxing drgandenseresidualgenerativeadversarialnetworkforimageenhancementinanunderwaterautonomousdrivingdevice |