SURE-Based Non-Local Means
SURE-Based Non-Local Means
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DOI：
10.1109/lsp.2009.2027669
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发表时间：
2009-11-01
影响因子：
3.9
通讯作者：
Kocher, Michel
中科院分区：
文献类型：
--
作者：
Van De Ville, Dimitri;Kocher, Michel
非局部均值（NLM）为去噪提供了一个强大的框架。然而，该算法的一些参数（最值得注意的是平滑核的宽度）依赖于数据，难以调整。在这里，我们建议使用Stein的无偏风险估计（SURE），以监测的均方误差（MSE）的NLM算法恢复损坏的图像加性白色高斯噪声。SURE原理允许在不知道无噪声信号的情况下评估MSE。我们推导出一个显式的解析表达式SURE的设置NLM，可以在低计算成本的实现中纳入。最后，我们提出的实验结果，确认所提出的参数选择的最优性。
Non-local means (NLM) provides a powerful framework for denoising. However, there are a few parameters of the algorithm-most notably, the width of the smoothing kernel-that are data-dependent and difficult to tune. Here, we propose to use Stein's unbiased risk estimate (SURE) to monitor the mean square error (MSE) of the NLM algorithm for restoration of an image corrupted by additive white Gaussian noise. The SURE principle allows to assess the MSE without knowledge of the noise-free signal. We derive an explicit analytical expression for SURE in the setting of NLM that can be incorporated in the implementation at low computational cost. Finally, we present experimental results that confirm the optimality of the proposed parameter selection.