计算机科学 ›› 2012, Vol. 39 ›› Issue (Z6): 513-514.

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基于二代Curvelet变换和区域匹配度的图像融合算法

邓 艾,吴 谨,杨 萃,李 娟   

  1. (武汉科技大学信息科学与工程学院 武汉430081)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Image Fusion Algorithm Based on Second Generation Curvelet Transform and Regional Matching Degree

  • Online:2018-11-16 Published:2018-11-16

摘要: 提出了一种新的基于二代Curvelet变换的多传感器图像融合算法,分别讨论了粗尺度系数和细尺度系数的融合规则。首先采用二代Curvelet变换对源图像进行多尺度的分解,将粗尺度系数值进行变换使其强度分布一致,再采用加权平均的方法确定粗尺度融合系数。采用显著性测度和区域匹配度联合分析的方法确定细尺度系数,并进行一致性验证,最后进行二代Curvelet逆变换获取融合图像。将传统融合规则和该方法从独立因素、联合因素以及综合评价3方面进行了比较,结果表明,该方法较好地保持了边缘信息,减少了细节信息的损失,具有较优的性能参数和良好的视觉效果。

关键词: 图像融合,曲波变换,区域匹配度

Abstract: This text puts forwards a new multi-sensor image fusion algorithm based on the second-generation Curvelet transform and respectively discusses the fusion rules of coarse scale coefficients and fine scale coefficients. First make original images multi scale decomposed using Curvclet transform,and then transform coarse scale coefficients to equalize their strength distribution. Whereafter use the weighted average method to determine coarse scale fused coefficients and significant measure and regional matching degree joint analysis method to determine fine scale fused coefficients. Final1y, carry out consistency verification and inverse transform to acquire the fused image. The comparison between the traditional method and this new method is made from the three aspects:independent factors,united factors and comprehensive evaluation. The experiment proved the usefulness of the method, which is able to keep the edges, obtain performance parameter and better visual effect.

Key words: Image fusion,Curvelet transform,Regional matching degree

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