DRGAN:密集的残留生成对抗网络用于在水下自动驾驶装置中的图像增强
Jin Qian1, Hui Li1, Bin Zhang1
1College of Information Engineering, Taizhou University, Taizhou 225300, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
我们开发了一个密集的残留生成对抗网络 (DRGAN) 来增强水下图像. 这种方法有效地消除了度,并改善了自主水下车辆捕获的视觉图像中的色彩平衡.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 图像处理 图像处理
- 海洋学 海洋学 海洋学
背景情况:
- 自主水下车辆 (AUV) 使用视觉传感器进行导航.
- 水下环境存在诸如光散射和吸收等挑战,导致图像度和颜色偏差.
- 现有的方法很难有效地恢复水下图像的清晰度和颜色精度.
研究的目的:
- 提出一种新的深度学习模型,用于增强水下图像.
- 为了解决水下视觉中颜色偏差和高度的问题.
- 为了提高水下自动驾驶设备中使用的视觉传感器的性能.
主要方法:
- 一个密集的剩余生成对抗网络 (DRGAN) 已经开发出来.
- DRGAN包含一个多尺度的特征提取模块,用于全面的信息收集.
- 密集的残余块被用于特征交互和稳定的连接,形成一个循环网络,训练有多个损失函数.
主要成果:
- 在水下图像中,DRGAN有效地去除了水下图像的高度.
- 与现有技术相比,拟议的方法实现了优越的色彩均等.
- 使用RUIE和Underwater ImageNet数据集进行实验验证.
结论:
- 在水下图像增强方面,DRGAN提供了显著的进步.
- 这项技术可以提高AUV和其他水下视觉系统的可靠性和有效性.
- 在水下成像中,DRGAN成功克服了光散射和光吸收的局限性.
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