Image zooming or upsampling is a widely used tool in image processing and an essential step in many algorithms. Upsampling increases the number of pixels and introduces new information into the image, which can lead to numerical effects such as ringing artifacts, aliasing effects, and blurring of the image. In this paper, we propose an efficient polynomial interpolation algorithm based on the WENO algorithm for image upsampling that provides high accuracy in smooth regions, preserves edges and reduces aliasing effects. Although this is not the first application of WENO interpolation for image resampling, it is designed to have comparable complexity and memory load with better image quality than the separable WENO algorithm. We show that the algorithm performs equally well on smooth 2D functions, artificial pixel art, and real digital images. Comparison with similar methods on test images shows good results on standard metrics and also provides visually satisfactory results. Moreover, the low complexity of the algorithm is ensured by a small local approximation stencil and the appropriate choice of smoothness indicators.

翻译：图像缩放或上采样是图像处理中广泛使用的工具，也是许多算法中的关键步骤。上采样会增加像素数量并向图像引入新信息，可能导致振铃伪影、混叠效应和图像模糊等数值影响。本文提出一种基于WENO算法的高效多项式插值算法用于图像上采样，该算法在平滑区域具有高精度，同时能保持边缘并减少混叠效应。尽管这并非WENO插值在图像重采样中的首次应用，但其设计在保持与可分离WENO算法相当的计算复杂度和内存占用的同时，实现了更优的图像质量。我们证明该算法在光滑二维函数、人工像素艺术和真实数字图像上均表现良好。与测试图像上类似方法的比较显示，该算法在标准指标上取得良好结果，并提供了视觉上令人满意的效果。此外，通过采用小型局部逼近模板和适当选择平滑度指标，确保了算法的低复杂度。