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.
bilateral markov mesh random field image restoration markov random fields image modeling image processing texture analysis causal markov random field stochastic image analysis