GVF-based anisotropic diffusion models.

Hongchuan Yu; Chin-Seng Chua

doi:10.1109/tip.2006.871143

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GVF-based anisotropic diffusion models.

Hongchuan Yu·Chin-Seng Chua

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Abstract

In this paper, the gradient vector flow fields are introduced in image restoration. Within the context of flow fields, the shock filter, mean curvature flow, and Perona-Malik equation are reformulated. Many advantages over the original models can be obtained; these include numerical stability, large capture range, and high-order derivative estimation. In addition, a fairing process is introduced in the anisotropic diffusion, which contains a fourth-order derivative and is reformulated as the intrinsic Laplacian of curvature under the level set framework. By applying this fairing process, the shape boundaries will become more apparent. In order to overcome numerical errors, the intrinsic Laplacian of curvature is computed from the gradient vector flow fields instead of the observed images.

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Algorithms Anisotropy Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Information Storage and Retrieval Models, Statistical Reproducibility of Results Sensitivity and Specificity Journal Article Research Support, Non-U.S. Gov't

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Hongchuan Yu, Chin-Seng Chua. 2006. GVF-based anisotropic diffusion models.. https://doi.org/10.1109/tip.2006.871143

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