Super-resolution estimation of edge images

A hidden Markov model, which describes the evolution of a (binary) edge-image along the resolution axis, is presented. The model integrates two layers: A hidden layer consists of sources having the ability of “breeding” along the resolution axis according to a Markovian rule. A second layer consists of a Gibbs random field which is defined by all the sources. The available image is a realization of this field. After fitting such a model to a given pyramid, it is possible to estimate the super-resolution images by synthesizing additional levels of the process which created the pyramid. The hidden Markov model is found to be a useful tool, allowing us to incorporate selected properties in the process of evolution along the resolution axis, while simultaneously providing an interpretation of this process. The properties incorporated into the model significantly influence the super-resolution image