In this paper, we propose a novel approach for quantifying the noise level at each location of a digital signal. This method is based on replacing the conventional kernel-based approach extensively used in signal filtering by an approach involving another kind of kernel: a possibility distribution. Such an approach leads to interval-valued resulting methods instead of punctual ones. We show, on real and synthetic data sets, that the length of the obtained interval and the local noise level are highly correlated. This method is non-parametric and advantages over other methods since no assumption about the nature of the noise has to be hypothesized, except its local ergodicity.
Keywords. Signal processing, kernel methods, possibility distribution, noise quantization
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Authors addresses:
Kevin Loquin
118 Route de Narbonne
F-31062 Toulouse Cedex 9
Olivier Strauss
161, rue Ada
F-34392 Montpellier Cedex 5
E-mail addresses:
| Kevin Loquin | loquin@irit.fr |
| Olivier Strauss | strauss@lirmm.fr |