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    Multifocus Fusion with Multisize Windows

    机译:多尺寸Windows的Multifocus Fusion

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    The term fusion means in general an approach to combine the important information simultaneously from several sources (channels). When we approach image fusion, multiscale transforms (MST) are commonly used as the analyzing tool. It transforms the sources into a space-frequency domain which can be understood as a measure of the saliency (activity level). The criterion to fuse consists of taking the decision to preserve the most salient data from the sources. In order to reduce sensitivity against noise the saliency is often averaged over certain neighborhood (window). However averaging produces that decisions become more fuzzy. Traditionally the size of the neighborhood is chosen fixed according to the level of noise present in the sources, which has to be estimated in advance. This paper proposes a novel technique which combines a set of decreasing averaging windows in order to exploit the advantages of each one. We call it multisize windows-based fusion. This technique apart from improving fusion results avoids selecting the neighboring size in advance (and therefore to estimate the level of noise) since it only needs a simple set of windows defined according to image size. We compared it with another technique developed by us called oriented windows which, although it consider a fixed neighborhood, adapts the averaging shape to the spatial orientation of the saliency. The specific case of multifocus image fusion is considered for the experiments. The multisize windows technique delivers the best percentage of correct decisions compared with any single fixed window in all the experiments carried out, adding different noise sources (Gaussian, speckle and salt&pepper) with different levels. Although it does not performs better than the oriented window scheme one has to bear in mind that oriented windows are tuned in each case to the best size.
    机译:术语“融合”通常是指同时合并来自多个来源(渠道)的重要信息的方法。当我们进行图像融合时,多尺度变换(MST)通常被用作分析工具。它将源转换为空频域,这可以理解为对显着性(活动水平)的度量。融合的标准包括做出决定,以保留来自源的最重要的数据。为了降低对噪声的敏感性,通常在某些邻域(窗口)上对显着性进行平均。但是,平均会导致决策变得更加模糊。传统上,邻域的大小是根据源中存在的噪声级别选择固定的,必须事先对其进行估计。本文提出了一种新颖的技术,该技术结合了一组递减的平均窗口,以利用每个窗口的优势。我们称其为基于Windows的多尺寸融合。由于该技术仅需要根据图像大小定义的一组简单窗口,因此,除了改善融合结果之外,该技术还避免了提前选择相邻大?。ù佣兰圃肷剑┑穆榉?。我们将其与我们开发的另一种技术称为定向窗口进行了比较,该技术虽然考虑了固定邻域,但使平均形状适应显着性的空间方向。实验中考虑了多焦点图像融合的具体情况。在所有进行的实验中,与任何单个固定窗口相比,多尺寸窗口技术可提供最佳百分比的正确决策,并添加了不同级别的不同噪声源(高斯,斑点和椒盐)。尽管它的性能并不比定向窗口方案更好,但必须记住,定向窗口在每种情况下都已调整到最佳大小。

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