Abstract
In any image processing involving images having scale-dependent structure, a key challenge is the modeling of these multi-scale characteristics. Because single Gauss-Markov models are effective at representing only single-scale phenomena, the classic Hidden Markov Model can not perform well in the processing of more complex images, particularly near-fractal images which frequently occur in scientific imaging. Of further interest is the presence of space-variable, nonstationary behaviour. By constructing hierarchical hidden fields, which label the behaviour type, we are able to capture heterogeneous structure in a scale-dependent way. We will illustrate the approach with a method of frozen-state simulated annealing and will apply it to the resolution enhancement of porous media images.
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Liu, Y., Fieguth, P. (2009). Image Resolution Enhancement with Hierarchical Hidden Fields. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_8
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DOI: https://doi.org/10.1007/978-3-642-02611-9_8
Publisher Name: Springer, Berlin, Heidelberg
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