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arXiv 1403.2131math.NAcs.NA
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各向异性扩散在曲面上的应用

Anisotropic Diffusion on Curved Surfaces

Leidos Health(莱多斯健康)

Emma Naden, Thomas März, Colin B. Macdonald

AI总结:

本文提出在曲面上进行各向异性扩散的方法,用于图像去噪和艺术效果生成,通过曲面内禀方法处理非线性扩散方程,适用于各类曲面。

摘要:

本文提出在曲面上进行各向异性扩散的方法,用于图像去噪和艺术效果生成,通过曲面内禀方法处理非线性扩散方程,适用于各类曲面。

英文摘要:

We demonstrate a method for filtering images defined on curved surfaces embedded in 3D. Applications are noise removal and the creation of artistic effects. Our approach relies on in-surface diffusion: we formulate Weickert's edge/coherence enhancing diffusion models in a surface-intrinsic way. These diffusion processes are anisotropic and the equations depend non-linearly on the data. The surface-intrinsic equations are dealt with the closest point method, a technique for solving partial differential equations (PDEs) on general surfaces. The resulting algorithm has a very simple structure: we merely alternate a time step of a 3D analog of the in-surface PDE in a narrow 3D band containing the surface with a reconstruction of the surface function. Surfaces are represented by a closest point function. This representation is flexible and the method can treat very general surfaces. Experimental results include image filtering on smooth surfaces, open surfaces, and general triangulated surfaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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