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Image Fusion for Enhanced Visualization: A Variational Approach

机译:图像融合以增强可视化:一种变体方法

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We present a variational model to perform the fusion of an arbitrary number of images while preserving the salient information and enhancing the contrast for visualization. We propose to use the structure tensor to simultaneously describe the geometry of all the inputs. The basic idea is that the fused image should have a structure tensor which approximates the structure tensor obtained from the multiple inputs. At the same time, the fused image should appear ‘natural’ and ‘sharp’ to a human interpreter. We therefore propose to combine the geometry merging of the inputs with perceptual enhancement and intensity correction. This is performed through a minimization functional approach which implicitly takes into account a set of human vision characteristics.
机译:我们提出一种变分模型,以执行任意数量的图像融合,同时保留重要信息并增强可视化对比度。我们建议使用结构张量同时描述所有输入的几何形状。基本思想是,融合图像应具有近似于从多个输入获得的结构张量的结构张量。同时,对于人工翻译,融合后的图像应显示为“自然”和“清晰”。因此,我们建议将输入的几何合并与感知增强和强度校正结合起来。这是通过最小化功能方法执行的,该方法隐式考虑了一组人类视觉特征。

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