Bayesian multifractal signal denoising

Abstract : 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.
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Communication dans un congrès
ICASSP03, IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2003, Hong-Kong, China. 2003
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Jacques Lévy Véhel, Pierrick Legrand. Bayesian multifractal signal denoising. ICASSP03, IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2003, Hong-Kong, China. 2003. 〈inria-00576482〉

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