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首页> 外文会议>Information Sciences and Systems, 2009. CISS 2009 >Theoretical analysis of correlation-based quality measures for weighted averaging image fusion
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Theoretical analysis of correlation-based quality measures for weighted averaging image fusion

机译：基于相关性的加权平均图像融合质量度量的理论分析

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摘要

Recently introduced correlation-based quality measures have received lots of attention due to the fact that they do not need ground-truth reference images to evaluate the performance of image fusion algorithms. In this paper we focus on theoretical analysis of these correlation-based quality measures when they are used to judge the performance of weighted averaging image fusion algorithms. The purpose of this paper is to rigorously prove that the correlation-based quality measures have some undesired behavior under certain conditions. We employ a statistical model for the observed sensor images and study the properties of these correlation-based quality measures. Our analysis shows that when we change the power of the desired signal or the noise in the input images, these correlation-based quality measures exhibit bad behaviors in some cases, indicating higher quality when lower quality is evident. The sufficient conditions for when the undesired behaviors occur and the intuitive explanation for our observation are given in this paper. Investigations with real images also demonstrate the utility of the theoretical analysis, by illustrating its predictive capabilities.
机译：最近引入的基于相关性的质量度量由于不需要地面真相参考图像来评估图像融合算法的性能而备受关注。在本文中，我们专注于对这些基于相关性的质量度量进行加权平均图像融合算法性能评估的理论分析。本文的目的是严格证明在特定条件下基于相关性的质量度量具有某些不良行为。我们对观察到的传感器图像采用统计模型，并研究这些基于相关性的质量度量的属性。我们的分析表明，当我们改变输入图像中所需信号的功率或噪声时，这些基于相关性的质量度量在某些情况下会表现出不良行为，表明当质量明显降低时质量就更高。本文给出了何时发生不良行为的充分条件以及我们观察的直观解释。对真实图像的研究还通过说明其预测能力来证明理论分析的实用性。

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