Image resampling is a widely used tool in image processing. The upsampling increases the number of pixels and introduces new information to the image which can have undesired effects, like ringing artifacts and oscillations, aliasing “jagged” lines effect, or introduces too much numerical diffusion. Histopolation upsampling methods produce much sharper images but are more prone to aliasing and ringing effect and oscillations which appear as spurious signals near sharp transitions in color intensity. In this paper, we propose an efficient and fast quasi-histopolation algorithm based on the Canonical Complete Chebyshev–Schoenberg operator approximations, applied dimension by dimension. These approximations, because of their shape preserving properties, avoid oscillations. Presented methods have several tunable parameters that control tension and shape properties of the approximation which are used to reduce the aliasing effect while keeping the image visually sharp. Numerical tests on real and artificial images demonstrate the effectiveness and show the computational efficiency of the proposed algorithm.

中文翻译：

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CCC-Schoenberg算子在图像重采样中的应用

图像重采样是图像处理中广泛使用的工具。上采样会增加像素数量并为图像引入新信息，这些信息可能会产生不良影响，例如振铃伪影和振荡、锯齿状“锯齿”线效果或引入过多的数值扩散。直方插值上采样方法产生更清晰的图像，但更容易出现混叠和振铃效应以及在颜色强度急剧转变附近表现为虚假信号的振荡。在本文中，我们提出了一种高效且快速的准组织插补算法，该算法基于规范完全切比雪夫-勋伯格算子近似，逐维应用。由于它们的形状保持特性，这些近似避免了振荡。所提出的方法具有几个可调节参数，用于控制近似的张力和形状属性，用于减少混叠效应，同时保持图像在视觉上清晰。对真实图像和人工图像的数值测试证明了该算法的有效性和计算效率。