降解意识的多阶段融合用于水下图像增强
Lian Xie1,2, Hao Chen2, Jin Shu2
1School of Civil Engineering, Hunan City University, Yiyang 413000, China.
Journal of imaging
|January 27, 2026
概括
这项研究引入了实时水下图像增强的两阶段框架,改善了视觉退化中的清晰度和颜色. 该系统有效地解决了颜色投影,低光和模糊,使其适合边缘设备.
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:
- 水下图像遭受了显著的退化,包括颜色抛光,低照明和模糊.
- 这些问题源于波长依赖的水下光的吸收和散射.
- 现有的增强方法可能缺乏在资源有限的设备上实时应用的效率或适应性.
研究的目的:
- 开发一个实用的,实时的,两阶段的框架来增强水下图像.
- 优先考虑在边缘设备上部署,以实现实际的水下成像应用.
- 创建一个能够处理多种类型的图像损坏的降解意识系统.
主要方法:
- 在第一阶段使用轻量级的卷积神经网络 (CNN) 将图像退化分为三个类 (色投,低光,模糊).
- 第二阶段使用特定场景的增强管道和输出融合,使用线性融合和LiteUNetFusion模块.
- LiteUNetFusion使用了空间适应权重,可选的残余校正用于精细增强.
主要成果:
- 在EUVP数据集的一个子集上,CNN分类器实现了91.85%的准确性.
- 与单场景优化器相比,线性融合显著改善了性能指标 (PSNR,UCIQE,UIQM),具有较低的延迟.
- LiteUNetFusion进一步增强了结果,比线性融合改进了PSNR+1.5dB,并在混合降解场景中保留了局部细节.
结论:
- 拟议的两阶段框架为退化水下图像提供了有效和高效的实时增强.
- 模块化设计和轻量级组件使其适合在边缘设备上部署.
- 需要进一步的研究来解决诸如偶尔模糊/噪声放大和域泛化等局限性.
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