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Nonlocal Evolutions for Image Regularization

机译:图像正则化的非局部演化

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摘要

A nonlocal quadratic functional of weighted differences is examined. The weights are based on image features and represent the affinity between different pixels in the image. By prescribing different formulas for the weights, one can generalize many local and nonlocal linear denoising algorithms, including nonlocal means and bilateral niters. The steepest descent for minimizing the functional can be interpreted as a nonlocal diffusion process. We show state of the art denoising results using the nonlocal flow.
机译:检验了加权差异的非局部二次函数。权重基于图像特征,并表示图像中不同像素之间的亲和力。通过规定不同的权重公式,可以推广许多局部和非局部线性降噪算法,包括非局部均值和双线性。用于使功能最小化的最陡下降可以解释为非局部扩散过程。我们显示了使用非局部流的最新去噪结果。

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