水下图像增强使用扩散模型与对抗性学习
Xueyan Ding1,2, Xiyu Chen1, Yixin Sui1
1School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China.
Journal of imaging
|July 25, 2025
概括
本研究介绍了对抗性学习扩散水下图像增强 (ALDiff-UIE),这是改善水下图像质量的新方法. ALDiff-UIE有效地解决了低对比度,色彩扭曲和噪声问题,性能优于现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 水下图像由于环境因素而遭受低对比度,色彩扭曲和噪音.
- 现有的水下图像增强方法缺乏对各种水下条件的概括性.
研究的目的:
- 开发一种强大且可通用的水下图像增强方法.
- 改进水下图像的视觉质量和定量指标.
主要方法:
- 引入了对抗性学习扩散水下图像增强 (ALDiff-UIE),一种基于扩散模型的方法.
- 采用对抗式学习策略,使用生成器和区分器来消除噪音和提高质量.
- 整合了一个多尺度的动态窗口注意力机制,以实现有效的特征融合.
主要成果:
- ALDiff-UIE在水下图像增强方面取得了显著的改进.
- 在基准数据集中,PCQI平均增加了12.8%,UIQM平均增加了15.6%.
- 在定性和定量评估中表现优于几种主流的水下图像增强方法.
结论:
- 与现有的方法相比,ALDiff-UIE为增强水下图像提供了一种优越的解决方案.
- 拟议的方法在各种水下环境中显示出强大的概括能力.
- ALDiff-UIE有效地提高了水下图像的视觉质量和定量指标.
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