BA-GAN: Block Attention GAN model for Underwater Image Enhancement
文献类型：会议论文
| 作者 | Wang Y(王悦)2,3,4; Fan HJ(范慧杰)3,4 ; Liu SB(刘世本)2; Liu JX(刘佳鑫)1; Li, Sun6; Tang YD(唐延东)3,4
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| 出版日期 | 2021 |
| 会议日期 | October 15-17, 2021 |
| 会议地点 | Beijing, China |
| 关键词 | Generative Adversarial Networks underwater color image enhancement Channel and Spatial Attentions |
| 页码 | 809-813 |
| 英文摘要 | Since underwater images are an important route to underwater information, underwater image enhancement becomes an important fundamental study to underwater vision tasks. Regarding problems with underwater degraded images, such as color distortion causing image rendering blue or green, blur and low contrast leading to image loss of detail information, we propose a Generative Adversarial Network (GAN) based model that adds an attention mechanism to enhance underwater images. The model finally achieves the purpose of generating clear underwater images by learning the relationship between underwater degraded images and ground truth values, removing color interference in the image and enhancing the details of the image. We compared the proposed model with the the state-of-the-art methods on both EUVP and Underwater Image Enhancement Benchmark Dataset, experimental results on both datasets prove that our method performs more stable than state-of-the-art methods. |
| 源文献作者 | Beijing Institute of Technology ; Chinese Institute of Command and Control (CICC) ; IEEE Beijing Section ; Tongji University |
| 产权排序 | 1 |
| 会议录 | Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-0-7381-4657-7 |
| 源URL | [http://ir.sia.cn/handle/173321/30354]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Wang Y(王悦) |
| 作者单位 | 1.College of Electronic and Information Engineering, Liaoning Technical University, Huludao, China 2.University of Chinese Academy of Sciences, Shenyang, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 5.State Grid Shandong Electric Power Company, Jinan, China 6.State Grid Liaoning Electric Power Research Institute, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Wang Y,Fan HJ,Liu SB,et al. BA-GAN: Block Attention GAN model for Underwater Image Enhancement[C]. 见:. Beijing, China. October 15-17, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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