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The equivalence conditions of Wavelet Shrinkage and Anisotropic Diffusion and it's application in denosing

机译:小波收缩与各向异性扩散的等价条件及其在去噪中的应用

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The Wavelet Shrinkage distinguish between signals and noises according to their wavelet coefficient's amplitude, thus removing noises from noised signals by means of shrinkage. Anisotropic Diffusion diffuse signals according to gradient's direction and amplitude by different degree at different direction, removing the noises from noised signals while protecting signals. In this paper, the equivalence condition of Wavelet Shrinkage and Anisotropic Diffusion was proposed. Then the signals denoising algorithm based on the equivalence of Wavelet Shrinkage and Anisotropic Diffusion was proposed. The results show that the new algorithm combines the advantage of Wavelet Shrinkage and Anisotropic Diffusion, therefore it possesses the better ability of denoising and keeping the high frequency property of signals.
机译:小波收缩根据信号和噪声的小波系数幅度来区分信号和噪声,从而通过收缩从噪声信号中去除噪声。各向异性扩散根据梯度的方向和幅度在不同的方向上不同程度地扩散信号,从而在保护信号的同时消除了噪声信号中的噪声。提出了小波收缩与各向异性扩散的等价条件。提出了一种基于小波收缩和各向异性扩散等价性的信号去噪算法。结果表明,该算法结合了小波收缩和各向异性扩散的优点,具有较好的去噪能力和保持信号的高频特性。

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