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基于自适应融合的深度学习框架,用于使用多尺度注意力特征恢复水下图像质量.

T Veeramakali1, Md Shohel Sayeed1, Sumendra Yogarayan2

  • 1Centre for Intelligent Cloud Computing, COE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Malaka, 75450, Malaysia.

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本研究介绍了使用多尺度注意力特征 (ERUI-MSAF) 模型有效恢复水下图像的方法. ERUI-MSAF模型有效地提高了水下图像的可见性和质量,优于现有的方法.

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适应性的双边过.深度学习是一种深度学习.多层次的注意力特征具有多层次的注意力特征.恢复 恢复 恢复 恢复水下图片 水下图片

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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:

  • 水下图像遭受着像模糊,低对比度和色彩偏差这样的退化.
  • 恢复水下图像对于各种实际应用至关重要.
  • 传统的方法与复杂的水下图像退化作斗争.

研究的目的:

  • 开发一种有效的方法来恢复水下图像.
  • 提高水下图像的可见性和整体质量.
  • 引入使用多尺度注意特征 (ERUI-MSAF) 模型的水下图像高效恢复.

主要方法:

  • 适应双边过 (ABF) 用于降低噪声和预处理.
  • ERUI-MSAF模型整合了通道和空间注意力特征.
  • 空间特征的深波网络 (DWN) 和通道特征的高效网络 (EfficientNet) 的融合.

主要成果:

  • 在ERUI-MSAF模型中,可自适应地强调信息特征和区域.
  • 在EUVP和UIEB数据集上实现了 34.258 和 29.0073 的高峰信号噪声比 (PSNR) 值.
  • 与现有模型相比,证明了高性能和计算效率.

结论:

  • 拟议的ERUI-MSAF模型对于水下图像恢复是有效的.
  • 多尺度注意力特征的整合显著提高了图像质量.
  • 该方法为增强水下图像提供了一个有希望的解决方案.