Factor graphs for image processing

Here, we turn our attention to factor graphs and examine their message passing properties for image processing tasks. To this end, we focus on the maximum a posteriori (MAP) inference process in multi-layered graphs and exploit the ability of factor graphs to capture subtle interactions between image tokens, i.e. pixels, super pixels, features, etc. This leads to a general, yet simple belief propagation scheme. The benefits of doing this are two-fold. Firstly, this yields the ability to perform more accurate joint probability inference tasks at minimal additional computational cost. Secondly, we gain the advantage of modelling structural interactions between image tokens more accurately on graphical models with multiple levels of interaction (layers). We illustrate the use of factor graphs for image defogging and segmentation and compare our results against other techniques elsewhere in literature.