Wavelet-based multiresolution stochastic image models
Jun Zhang, Dongyan Wang, Que Tran · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
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 level of a wavelet expansion. Texture synthesis experiments, performed by using wavelet autoregressive and radial basis function (RBF) models, have produced promising results. Both models are relatively simple in each resolution and are better than single resolution models in capturing long range correlations. In texture synthesis experiments, the RBF models, especially the non-causal model, provide good visual resemblance to the original for relatively complex textures.