Bayesian multifractal signal denoising
Bayesian multifractal signal denoising
复制标题
贝叶斯多重分形信号去噪
DOI：
10.1109/icassp.2003.1201647
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
P. Legrand
中科院分区：
文献类型：
--
作者：
J. L. Véhel;P. Legrand
提出了一种半参数框架下信号/图像去噪的方法。我们的模型是一个基于小波的模型，它本质上假设了一个最小的局部正则性。这一假设转化为对信号的多重分形谱的约束。然后在贝叶斯框架中使用这些约束来估计原始信号的小波系数与噪声信号的小波系数。我们的方案非常适合于处理不规则信号，如(多)分形信号，并有可能用于湍流、生物医学或地震数据的处理。
This work presents an approach for signal/image denoising in a semi-parametric frame. Our model is a wavelet-based one, which essentially assumes a minimal local regularity. This assumption translates into constraints on the multifractal spectrum of the signals. Such constraints are in turn used in a Bayesian framework to estimate the wavelet coefficients of the original signal from the noisy ones. Our scheme is well adapted to the processing of irregular signals, such as (multi-)fractal ones, and is potentially useful for the processing of e.g. turbulence, bio-medical or seismic data.