优化了UNet框架,具有联合损失功能,用于水下图像增强
Xin Wang1, Zhonghua Luo2, Wei Huang3
1College of Computer Science and Software Engineering, Hohai University, Nanjing, 211100, China. wang_xin@hhu.edu.cn.
Scientific reports
|March 2, 2025
概括
本研究介绍了一个优化的UNet框架 (OUNet-JL) 来增强低质量的水下图像,解决模糊,颜色问题和噪音,以获得更清晰的视觉理解.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 水下成像技术的研究.
背景情况:
- 由于各种环境因素,水下图像通常质量不佳.
- 提高水下图像质量对于水资源经济,生态保护和可持续发展的应用至关重要.
研究的目的:
- 为了解决低质量的水下图像中细节模糊,色彩失衡和噪声干扰的问题.
- 为优质的水下图像增强提出一个优化的UNet框架,具有联合损失功能 (OUNet-JL).
主要方法:
- 开发了一个多余模块 (MRM) 来改善细节特征表示.
- 引入了一个空间多尺度特征提取模块 (SMFM) 带有频道注意力来纠正色彩不平衡.
- 设计了一个强化-操作-减去 (SOSFM) 模块,用于降低噪音和纠正扭曲.
- 将四个损失函数集成到一个联合损失函数中,以实现高效的网络培训.
主要成果:
- 拟议的OUNet-JL框架在UIEB和UFO-120数据集上的最先进算法相比,表现出更高的性能.
- 废弃性研究证实了单个模块 (MRM,SMFM,SOSFM) 的有效性.
结论:
- 通过解决关键的退化问题,OUNet-JL框架有效地提高了水下图像质量.
- 拟议的方法在水下图像恢复技术方面取得了重大进展.
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