Estimation of the Noise Level Function in Multivariate Images using the Tree of Shapes and non-parametric statistics



Nowadays, a lot of image processing applications need to know the noise level of an image to take it into account in these processes or to remove it. To do so, we developed a method to estimate the noise level, modeled by the noise level function, for grayscale images, and then for multivariate images, using simplifying hypotheses. This semester, we introduced new tools to improve this method and to remove the simplifying hypotheses defined the last semester.