用于水下图像增强的Mamba-convolution混合网络
Hailan Chen1, Yijian Wang2,3, Lihua Wu4
1School of Science, Jimei University, Xiamen, 361021, China.
Scientific reports
|August 30, 2025
概括
这项研究引入了Mamba-Convolution网络用于水下图像增强 (MC-UIE),提高清晰度和颜色精度. 这种新方法有助于加强海洋生态监测和水下目标检测.
科学领域:
- 计算机视觉
- 海洋生物学
- 图像处理
背景情况:
- 由于海洋条件和照明,水下图像的清晰度低,颜色扭曲.
- 图像质量下降阻碍了海洋生态监测和水下目标检测.
研究的目的:
- 开发一种有效的水下图像增强方法.
- 为科学应用提高水下图像的质量.
主要方法:
- 一个Mamba-Convolution网络用于水下图像增强 (MC-UIE) 的开发.
- 使用标准卷积,Mamba-Convolution混合区块 (M-C HB) 与2D选择性扫描 (SS2D) 和特征注意模块 (FAM),以及交叉融合的Mamba区块 (CFMB).
主要成果:
- 该方法显著增强了全球和本地图像的依赖性.
- 与现有的方法相比,在颜色,照明和细节恢复方面取得了卓越的表现.
- 通过对主流数据集进行广泛的定性和定量实验来证明有效性.
结论:
- 拟议的MC-UIE方法在水下图像增强方面取得了重大进展.
- 这种方法有效地解决了海洋环境中图像质量不佳的挑战.
- 开发的网络有望改善海洋生态监测和水下目标识别.
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