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Optimally sparse image approximation by adaptive linear splines over anisotropic triangulations

机译:通过各向异性三角形术通过自适应线性样条的最佳稀疏图像近似

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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 Ho?lder 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:各向异性三角结构为稀疏图像表示提供有效的方法。在以前的工作中,我们提出了一种用于稀疏图像近似,自适应变薄的本地自适应算法,其依赖于各向异性Delaunay三角形上的线性样条。在这一贡献中,我们通过各向异性保形三角形通过线性样条来解决关于图像近似的理论和实践方面。我们的讨论包括关于相关类别功能的渐近最佳的N级近似值,例如αHoα的地平线函数,α> 2 / p-1的α> 2 / p-1的常规功能。此外,我们通过数值示例和比较展示了我们的自适应变薄算法的良好性能。

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