Application of Markov random fields to smoothing and segmentation of noisy pictures

An algorithm is presented for smoothing piecewise stationary data from measurements corrupted by additive noise. Its main feature is the combination of Markov random field models, with Kalman filtering techniques and dynamic programming in order to smooth and segment the data within the regions of stationarity without affecting the edges. Applications to one-dimensional and two-dimensional data are given, with particular emphasis on the segmentation of multiregion images. Although application to piecewise constant data are emphasized, the algorithm can be extended to data with regions characterized by textures with which different autoregressive models are associated