MixRformer:双分支网络用于在波形域中的水下图像增强
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究介绍了MixRformer,一种水下图像增强模型. 它使用波形变换和混合架构来改进颜色和细节,优于其他方法.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 水下图像会受到色彩扭曲和低可见度的影响.
- 现有的卷积神经网络 (CNN) 模型具有有限的全球建模能力.
- 变压器模型在弱局部特征提取和高计算成本方面扎.
研究的目的:
- 开发一个有效的水下图像增强模型.
- 为了解决当前CNN和变压器模型的局限性.
- 改进全球和本地特征建模用于水下图像.
主要方法:
- 拟议的MixRformer模型结合波形变换和混合架构.
- 使用离散波波变换 (IWT/DWT) 进行多分辨率特征分解.
- 引入了一种双分支特征捕获模块 (DFCB),具有表面信息提取和一个封闭的变压器.
主要成果:
- 与主流方法相比,MixRformer在色彩恢复和细节增强方面表现出卓越的性能.
- 该模型实现了优化的计算效率.
- 实验结果验证了拟议架构的有效性.
结论:
- MixRformer为水下图像增强提供了一个有效的解决方案.
- 混合方法成功地平衡了全球和本地特征建模.
- 该模型提供了一种计算效率高的方法来提高水下图像质量.
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