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Optimally sparse image approximation by adaptive linear splines over anisotropic triangulations

机译:各向异性三角剖分上的自适应线性样条曲线最优稀疏图像逼近

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Anisotropic triangulations provide efficient methods for sparse image representations. In previous work, we have proposed a locally adaptive algorithm for sparse image approximation, adaptive thinning, which relies on linear splines over anisotropic Delaunay triangulations. In this contribution, we address theoretical and practical aspects concerning image approximation by linear splines over anisotropic conformal triangulations. Our discussion includes asymptotically optimal N-term approximations on relevant classes of target functions, such as horizon functions across α Hölder smooth boundaries and regular functions of W regularity, for α > 2/p-1. Moreover, we demonstrate the good performance of our adaptive thinning algorithm by numerical examples and comparisons.
机译:各向异性三角剖分为稀疏图像表示提供了有效的方法。在先前的工作中,我们提出了一种用于稀疏图像逼近,自适应细化的局部自适应算法,该算法依赖于各向异性Delaunay三角剖分上的线性样条。在这一贡献中,我们讨论了在各向异性共形三角剖分上通过线性样条进行图像逼近的理论和实践方面。我们的讨论包括目标函数的相关类的渐近最优N项逼近,例如,对于α> 2 / p-1,跨越αHölder光滑边界的水平函数和W正则性的正则函数。此外,我们通过数值示例和比较证明了我们的自适应稀疏算法的良好性能。

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