Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download
Contributor : Lisandro Fermin Connect in order to contact the contributor
Submitted on : Tuesday, March 15, 2011 - 5:58:27 PM
Last modification on : Friday, January 21, 2022 - 3:20:45 AM
Long-term archiving on: : Thursday, June 16, 2011 - 2:30:49 AM


Files produced by the author(s)


  • HAL Id : inria-00576482, version 1



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⟩



Les métriques sont temporairement indisponibles