Compound Markov random field model of signals on graph: an application to graph learning
In this work we address the problem of Signal on Graph (SoG) modeling, which can provide a powerful image processing tool for suitable SoG construction. We propose a novel SoG Markovian model suited to jointly characterizing the graph signal values and the graph edge processes. Specifically, we resort to the compound MRF called pixel-edge model formerly introduced in natural images modeling and we reformulate it to frame SoG modeling.