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分解多边过用于加快过,使用多个指导图像.

Haruki Nogami1, Yamato Kanetaka1, Yuki Naganawa1

  • 1Department of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan.

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PubMed
概括

本研究介绍了使用多个引导图像进行边缘保护过的高效算法,增强了传感器融合应用. 这种新方法,即分解多边过 (DMF),可以加快处理速度,同时有效利用多个数据源.

关键词:
恒定时间过.边缘保护过器过器多边过的多边过.

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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
  • 信号处理 信号处理

背景情况:

  • 多模式信号处理和传感器融合在现代图像传感中至关重要.
  • 边缘保护过对于场景属性估计和反向染等应用至关重要.
  • 现有的加速过器很难有效地利用多个引导图像.

研究的目的:

  • 开发一种高效的边缘保护过算法,能够处理多个指导图像.
  • 扩展现有的高效过方法,以支持多边过.
  • 为了解决传统边缘保护过器的计算时间限制.

主要方法:

  • 提出了一个叫做分解多边过 (DMF) 的算法.
  • DMF将过过程分解为一系列恒定时间操作.
  • 扩展高效的边缘保护过器,以纳入多个指导图像.

主要成果:

  • 分解多边过 (DMF) 算法表现出高效率.
  • 该方法有效地利用多个指导图像进行改进的过.
  • 实验结果证实了该算法的适用于各种应用的适用性.

结论:

  • 拟议的分解多边过 (DMF) 算法为使用多个指导图像进行边缘保护过提供了有效的解决方案.
  • 这一进步有利于各种传感器融合应用,需要增强的图像处理.
  • 该算法成功克服了先前方法在处理多个指导数据方面的局限性.