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A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

机译:基于SFL - CONTOURLET变换的拟南芥细胞GFP图像和相位对比图像的融合算法

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A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones.
机译:本文提出了一种用于绿色荧光蛋白(GFP)图像和相位对比图像的混合多尺度和多级图像融合算法。组合强度 - 色调饱和度(IHS)变换和锐频定位轮廓变换(SFL-CT),该算法使用不同的详细子带的不同融合策略,包括邻域一致性测量(NCM),可以自适应地在彩色背景之间找到平衡灰色结构。还考虑了基于经验模型的两种邻域类。作为客观标准,引入了视觉信息保真度(VIF)以评估融合图像。来自John Innes Centre的117组拟南芥细胞图像的实验结果表明,新算法不仅可以使原始图像的细节保持良好保存,而且还提高了融合图像的可见性,这表明了新的方法对传统方法的优越性。

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