Noise quantization via possibilistic filtering

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 point-valued ones. We show, on real and artificial data sets, that the length of the obtained interval and the local noise level are highly correlated. This method is non-parametric and advantageous over other methods since no assumption about the nature of the noise has to be made, except its local ergodicity.