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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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https://hal.inria.fr/inria-00576482
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Submitted on : Tuesday, March 15, 2011 - 5:58:27 PM
Last modification on : Thursday, May 2, 2019 - 2:10:14 PM
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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. ⟨inria-00576482⟩

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