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Multivariate shrinkage functions for wavelet-based denoising

机译:多元收缩函数用于基于小波的去噪

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The first nonlinear rules for wavelet based image denoising assume wavelet coefficients are independent. However it is well known that there are strong dependencies between coefficients such as interscale and intrascale dependencies. We have introduced a non-Gaussian bivariate pdf that exploits the interscale dependencies between a coefficient and its parent. In this paper, how to extend this pdf in order to include the other dependencies will be discussed and in one example, a multivariate shrinkage rule will be derived. The good performance of this new rule will be illustrated on an image denoising algorithm which captures also interscale dependencies.
机译:基于小波的图像去噪的第一个非线性规则假设小波系数是独立的。但是,众所周知,系数之间存在很强的依存关系,例如尺度间和尺度内的依存关系。我们引入了一个非高斯双变量pdf,该pdf利用了系数与其父级之间的尺度间依赖关系。在本文中,将讨论如何扩展此pdf以包括其他依赖关系,并且在一个示例中,将得出多元收缩规则。该新规则的良好性能将在图像降噪算法中进行说明,该算法还捕获了尺度间相关性。

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