Wavelet-based multiresolution stochastic image models

Jun Zhang; Dongyan Wang; Que Ngoc Tran

doi:10.1117/12.271133

4 April 1997 Wavelet-based multiresolution stochastic image models

Jun Zhang, Dongyan Wang, Que Ngoc Tran

Proceedings Volume 3026, Nonlinear Image Processing VIII; (1997) https://doi.org/10.1117/12.271133
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

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.

Citation Download Citation

Jun Zhang, Dongyan Wang, and Que Ngoc Tran "Wavelet-based multiresolution stochastic image models", Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); https://doi.org/10.1117/12.271133

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