Bilateral Markov mesh random field and its application to image restoration

This paper introduces bilateral Markov mesh random field to overcome the shortcomings of the conventional Markov random fields in image modeling. These shortcomings consist of (a) the computational intractability of such fields when expressing the image probability function in the form of the Gibbs distribution function, and (b) the formulation of the image probability function via the product of low-dimensional densities at the expense of obtaining non-symmetrical image models. The properties of bilateral Markov mesh random field are presented and used to derive an image model to address the above shortcomings. As an application, a framework for image restoration is then provided. Restoration results based on this new bilateral Markov mesh random field are compared to the conventional fields to demonstrate its effectiveness.

► A new class of causal Markov random fields (MRFs) to generate symmetric MRFs.
► A computationally tractable and feasible image model.
► An image modeling approach for image processing and pattern recognition applications.
► A multi-level image restoration framework.