A generalization of improved susceptibility propagation

Abstract

Recently, an effective susceptibility propagation method for binary Markov random fields based on a concept of diagonal consistency was proposed. This improved susceptibility propagation is a powerful method and exhibits a robust performance for various types of network structures. In this paper, a generalization of the improved susceptibility propagation using an orthonormal function expansion is proposed in which any pairwise potential functions and multivalued random variables are acceptable. In the latter part of this paper, the proposed method is applied to a direct problem and an inverse problem on a generalized sparse prior, which is a recently proposed prior model for natural images.