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Coherence-enhancing diffusion filtering
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AI
DOI：10.1023/A:1008009714131.png)
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Abstract
En 中文
The completion of interrupted lines or the enhancement of flow-like structures is a challenging task in computer vision, human vision, and image processing. We address this problem by presenting a multiscale method in which a nonlinear diffusion filter is steered by the so-called interest operator (second-moment matrix, structure tensor). An m-dimensional formulation of this method is analysed with respect to its well-posedness and scale-space properties. An efficient scheme is presented which uses a stabilization by a semi-implicit additive operator splitting (AOS), and the scale-space behaviour of this method is illustrated by applying it to both 2-D and 3-D images.
Keywords:
image enhancement
scale-space
texture
nonlinear diffusion
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9.3
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4.0K
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2.8W
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