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多通道表示学习增强展开多尺度压缩传感网络,用于高质量的图像重建.
Chunyan Zeng1, Shiyan Xia1, Zhifeng Wang2
1Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China.
Entropy (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了用于压缩传感 (CS) 图像重建的多通道和多尺度展开网络 (MMU-Net). 通过使用多通道和多尺度的特征提取来提高性能,MMU-Net克服了现有方法的局限性.
科学领域:
- 信号处理 信号处理
- 图像重建 图像的重建
- 机器学习 机器学习
背景情况:
- 深度展开网络 (DUN) 广泛用于压缩传感 (CS) 重建.
- 现有的DUN仅限于单通道处理,阻碍了特征表征.
- 当前网络中的单级结构通过忽视多级特征来限制性能.
研究的目的:
- 引入一个新的CS重建网络,MMU-Net,解决现有DUN的局限性.
- 通过多道和多尺度处理来增强特征表征和重建性能.
主要方法:
- 开发了多道和多规模展开网络 (MMU-Net).
- 整合了Adap-SKConv,配备了注意力机制,用于增强功能地图表征.
- 引入了一个多尺度块,用于提取多尺度图像特征.
主要成果:
- 与最先进的CS重建方法相比,MMU-Net显示出更高的性能.
- 在不同的数据集上进行评估,包括Urban100,Set11,BSD68和UC Merced土地使用数据集.
- 实现了更好的图像表征和重建功能.
结论:
- 在CS重建中,MMU-Net有效地克服了单通道和单尺度方法的局限性.
- 拟议的多道和多尺度架构显著提高了重建性能.
- MMU-Net在自然和遥感图像重建应用中显示出前景.
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