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arXiv 1509.08082cs.CV
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多元中值滤波器与偏微分方程

Multivariate Median Filters and Partial Differential Equations

University for Health Sciences, Medical Informatics and Technology (UMIT)(健康科学、医学信息与技术大学)

Martin Welk

摘要:

多元中值滤波器已被提出作为推广的公认的中值滤波器的灰度值图像的多通道图像。作为多变量中位数,大多数最近的方法使用$L^1$中位数,即目标函数的最小值,即到所有输入点的距离之和。单变量中值滤波器的许多性质推广到这样的滤波器。然而,Guichard和Morel关于中值滤波近似平均曲率运动偏微分方程的著名结果对于$L^1$多元中值滤波并不具有非常简单的对应。我们讨论了仿射等变Oja中值和仿射等变变换--再变换$L^1$中值作为$L^1$中值滤波的替代。我们分析多元中值滤波器在空间连续的设置,包括制定一个空间连续的版本的变换-retransformation $L^1$中位数,并推导出偏微分方程近似这些过滤器的情况下,二维平面图像,三通道体积图像和三通道平面图像。仿射等变滤波器的偏微分方程可以在几何上解释为扩散和主成分曲率运动贡献与基于主成分扭转的交叉效应项的组合。数值实验证明了近似结果的有效性。

英文摘要:

Multivariate median filters have been proposed as generalisations of the well-established median filter for grey-value images to multi-channel images. As multivariate median, most of the recent approaches use the $L^1$ median, i.e.\ the minimiser of an objective function that is the sum of distances to all input points. Many properties of univariate median filters generalise to such a filter. However, the famous result by Guichard and Morel about approximation of the mean curvature motion PDE by median filtering does not have a comparably simple counterpart for $L^1$ multivariate median filtering. We discuss the affine equivariant Oja median and the affine equivariant transformation--retransformation $L^1$ median as alternatives to $L^1$ median filtering. We analyse multivariate median filters in a space-continuous setting, including the formulation of a space-continuous version of the transformation--retransformation $L^1$ median, and derive PDEs approximated by these filters in the cases of bivariate planar images, three-channel volume images and three-channel planar images. The PDEs for the affine equivariant filters can be interpreted geometrically as combinations of a diffusion and a principal-component-wise curvature motion contribution with a cross-effect term based on torsions of principal components. Numerical experiments are presented that demonstrate the validity of the approximation results.

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