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Separating reflection components of textured surfaces using a single image

机译:使用单个图像分离纹理表面的反射分量

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The presence of highlights, which in dielectric inhomogeneous objects are linear combination of specular and diffuse reflection components, is inevitable. A number of methods have been developed to separate these reflection components. To our knowledge, all methods that use a single input image require explicit color segmentation to deal with multicolored surfaces. Unfortunately, for complex textured images, current color segmentation algorithms are still problematic to segment correctly. Consequently, a method without explicit color segmentation becomes indispensable, and this paper presents such a method. The method is based solely on colors, particularly chromaticity, without requiring any geometrical parameter information. One of the basic ideas is to compare the intensity logarithmic differentiation of specular-free images and input images iteratively. The specular-free image is a pseudo-code of diffuse components that can be generated by shifting a pixel's intensity and chromaticity nonlinearly while retaining its hue. All processes in the method are done locally, involving a maximum of only two pixels. The experimental results on natural images show that the proposed method is accurate and robust under known scene illumination chromaticity. Unlike the existing methods that use a single image, our method is effective for textured objects with complex multicolored scenes.
机译:在介电非均匀物体中存在亮点的存在是镜面和漫射反射部件的线性组合,是不可避免的。已经开发了许多方法来分离这些反射组件。据我们所知,所有使用单个输入映像的方法都需要显式颜色分段来处理多色表面。不幸的是,对于复杂的纹理图像,当前的颜色分割算法正确仍然存在问题。因此,没有明确颜色分割的方法变得不可或缺,本文提出了这种方法。该方法仅基于颜色,特别是色度,而不需要任何几何参数信息。其中一个基本思想是比较镜面对数对数分化的镜布图像和输入图像的比较。镜面图像是漫射组件的伪码,可以通过在保持其色调的同时使像素的强度和色度变换来产生。该方法中的所有进程在本地完成,涉及最多两个像素。自然图像的实验结果表明,在已知的场景照明色度下,所提出的方法是准确和稳健的。与使用单个图像的现有方法不同,我们的方法对于具有复杂的多彩多姿的场景的纹理对象是有效的。

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