A Finite Markov Random Field approach to fast edge-preserving image recovery

We investigate the properties of edge-preserving smoothing in the context of Finite Markov Random Fields (FMRF). Our main result follows from the definition of discontinuity adaptive potential for FMRF which imposes to penalize linearly image gradients. This is in agreement with the Total Variation based regularization approach to image recovery and analysis. We also report a fast computational algorithm exploiting the finiteness of the field, it uses integer arithmetic and a gradient descent updating procedure. Numerical results on real images and comparisons with anisotropic diffusion and half-quadratic regularization are reported.