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首页> 外文期刊>IEEE Transactions on Information Theory >Singularity detection and processing with wavelets
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Singularity detection and processing with wavelets

机译:小波奇异性检测与处理

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The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images.
机译:审查了Lipschitz指数的奇异性的数学表征。回顾了从小波变换尺度上的演化估计函数的局部Lipschitz指数的定理。然后证明,小波变换模的局部最大值可以检测不规则结构的位置,并提供数值程序来计算其Lipschitz指数。具有快速振荡的奇异性的小波变换具有特殊的行为,需要单独研究。从小波变换模量最大值测量这种振荡的局部频率。从数值上表明,可以从它们的小波变换模量的​​局部最大值很好地近似重建一维和二维信号。作为一种应用,开发了一种算法,该算法通过分析跨尺度的小波变换最大值的演变来消除信号中的白噪声。在二维中,小波变换最大值指示图像中边缘的位置。

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