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Structure similarity based objective metric for pixel-level image fusion

机译:像素级图像融合的基于结构相似度的客观度量

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Pixel-level image fusion has been investigated in various applications and many algorithms have been proposed. However, few authors have addressed the problem of how to evaluate the performance of those algorithms. In this paper, we propose a structure similarity based objective metric for image fusion, which evaluates the similarity of the gradient fields between the source images and the fused result. Meanwhile, local information given by salience within localized windows are incorporated into the final metric. Extensive experiments demonstrate that the proposed metric can evaluate the image fusion performance effectively and is consistent with perceptually obtained results.
机译:已经在各种应用中研究了像素级图像融合,并且已经提出了许多算法。但是,很少有作者解决如何评估这些算法性能的问题。在本文中,我们提出了一种基于结构相似度的图像融合目标度量,用于评估源图像和融合结果之间梯度场的相似性。同时,在局部窗口中由显着性给出的局部信息被合并到最终度量中。大量实验表明,所提出的度量标准可以有效地评估图像融合性能,并且与在感知上获得的结果一致。

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