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IMAGE DENOISING USING WAVELETS TRANSFORM AND ADAPTIVE LEAST MEAN SQUARE METHOD

机译:使用小波变换和适应性最低均方法的图像去噪

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The wavelet Shrinkage is one of the nonlinear signal denoising method to remove Speckle noise by shrinking the empirical wavelet coefficients in the wavelet domain. In this paper, we propose the adaptive wavelet shrinkage which updates the threshold levels adaptively by Least Mean Square (LMS) type algorithm according to the reference signal. The adaptive wavelet Shrinkage Method is robust and it is also easy to control the filter action by modifying the threshold levels directly.
机译:小波收缩是通过在小波域中的经验小波系数缩小散射斑块噪声的非线性信号去噪方法之一。在本文中,我们提出了根据参考信号的最小均方(LMS)型算法自适应地更新阈值水平的自适应小波收缩。自适应小波收缩方法是稳健的,并且还通过直接修改阈值水平来易于控制滤波器动作。

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