Optimized edge detection using a priori models

Gibbs distributions are widely used as a priori density functions for Bayesian image restoration. When a Gibbs distribution prior is formulated to incorporate a line or edge process, Bayesian image restoration additionally yields the edges in the image. We interpret these a priori and density function as themselves being edge detectors. We formulate a density function that is more effective in this regard. This work explores the use of higher order Markov random fields that hold rich potential for varied applications