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A image denoising by modified nonlocal means method based on generalized cross-validation

机译:基于广义交叉验证的改进非局部均值方法去噪

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摘要

Nonlocal means method (NLM) is a powerful algorithm in image denoising area. In this article, firstly its modified expression is defined, and then generalized cross validation (GCV) method is used to approximate the optimal value of two parameters in this expression. At the same time, the analytical formula of GCV method can also be derived from modified expression. As a result, the restoration image processed by optimal value of parameters is mostly close to the original noise-free image and their mean square error (MSE) is nearly minimal compared with others. Finally, the experimental results show the performance of noise reduction by different methods.
机译:非局部均值方法(NLM)是图像去噪领域中一种功能强大的算法。在本文中,首先定义了它的修改表达式,然后使用广义交叉验证(GCV)方法来近似该表达式中两个参数的最优值。同时,GCV方法的解析公式也可以从修改后的表达式中导出。结果,由参数的最佳值处理的恢复图像大部分接近原始无噪声图像,并且与其他图像相比,它们的均方差(MSE)几乎最小。最后,实验结果表明了不同方法的降噪性能。

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