Wavelet-based multiresolution stochastic image models

In this paper, we describe a wavelet-based approach to multiresolution stochastic image modeling. The basic idea here is that a complex random field, e.g., one with long range and nonlinear spatial correlations, can be decomposed into several less complex random fields. This is done by defining a random field in each resolution of a wavelet expansion. Experiments, performed for the multiresolution AR (autoregressive) and RBF (radial basis function) models, have produced promising results. Specifically, the wavelet-AR model captures long range correlation better than the single resolution AR model, and for both the wavelet AR and RBF models, random fields in the wavelet domain do appear to be simpler to model than those on the finest resolution