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- W2165120217 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 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." @default.
- W2165120217 created "2016-06-24" @default.
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- W2165120217 date "2002-11-19" @default.
- W2165120217 modified "2023-09-26" @default.
- W2165120217 title "Wavelet-based multiresolution stochastic image models" @default.
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- W2165120217 doi "https://doi.org/10.1109/iscv.1995.477047" @default.
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