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Proceedings Paper

Wavelet-based multiresolution stochastic image models
Author(s): Jun Zhang; Dongyan Wang; Que Ngoc Tran
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Paper 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.

Paper Details

Date Published: 4 April 1997
PDF: 12 pages
Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); doi: 10.1117/12.271133
Show Author Affiliations
Jun Zhang, Univ. of Wisconsin/Milwaukee (United States)
Dongyan Wang, Univ. of Wisconsin/Milwaukee (United States)
Que Ngoc Tran, Univ. of Wisconsin/Milwaukee (United States)


Published in SPIE Proceedings Vol. 3026:
Nonlinear Image Processing VIII
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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