A Closed-Form Solution for Graph Signal Separation Based on Smoothness

Abstract

Using smoothness criteria to separate smooth graph signals from their summation is an approach that has recently been proposed (Mohammadi et al., 2023) and shown to have a unique solution up to the uncertainty of the average values of source signals. In this correspondence, closed-form solutions of both exact and approximate decompositions of that approach are presented. This closed-form solution in the exact decomposition also answers the open problem of the estimation error. Additionally, in the case of Gaussian source signals in the presence of additive Gaussian noise, it is shown that the optimization problem of that approach is equivalent to the Maximum A Posteriori (MAP) estimation of the sources.