isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion (算子)

名称

isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion — 对图像进行各向同性扩散。

签名

描述

算子 isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion performs an isotropic diffusion of the input image ImageImageImageImageimageimage. This corresponds to a convolution of the image matrix with a Gaussian mask of standard deviation SigmaSigmaSigmaSigmasigmasigma. If the parameter IterationsIterationsIterationsIterationsiterationsiterations is set to 0, such a convolution is performed explicitly. For input images with a full ROI, isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion returns the same results as the operator derivate_gaussderivate_gaussDerivateGaussDerivateGaussDerivateGaussderivate_gauss when choosing 'none'"none""none""none""none""none" for its parameter ComponentComponentComponentComponentcomponentcomponent. If the gray value matrix is larger than the ROI of ImageImageImageImageimageimage the two operators differ since derivate_gaussderivate_gaussDerivateGaussDerivateGaussDerivateGaussderivate_gauss takes the gray values outside of the ROI into account, while isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion mirrors the values at the boundary of the ROI in any case. The computational complexity increases linearly with the value of SigmaSigmaSigmaSigmasigmasigma。

If IterationsIterationsIterationsIterationsiterationsiterations has a positive value the smoothing process is considered as an application of the heat equation on the gray value function u with the initial value defined by the gray values of ImageImageImageImageimageimage at a time . This equation is then solved up to a time , which is equivalent to the above convolution, using an iterative procedure for parabolic partial differential equations. The computational complexity is proportional to the value of IterationsIterationsIterationsIterationsiterationsiterations and independent of SigmaSigmaSigmaSigmasigmasigma in this case. For small values of IterationsIterationsIterationsIterationsiterationsiterations, the computational accuracy is very low, however. For this reason, choosing IterationsIterationsIterationsIterationsiterationsiterations < 3 is not recommended.

For smaller values of SigmaSigmaSigmaSigmasigmasigma, the convolution implementation is typically the faster method. Since the runtime of the partial differential equation solver only depends on the number of iterations and not on the value of SigmaSigmaSigmaSigmasigmasigma, it is typically faster for large values of SigmaSigmaSigmaSigmasigmasigma if few iterations are chosen (e.g., IterationsIterationsIterationsIterationsiterationsiterations = 3).

有关平滑滤波器概念的说明，请参阅 滤波器 / 平滑 一章的引言。

注意

请注意，若使用域缩减后的图像作为输入，滤波器算子可能会返回意外结果。请参阅 滤波器 一章

执行信息

• 多线程类型：可重入（与非独占算子并行运行）。
• 多线程作用域：全局（可从任何线程调用）。
• 在元组级别上自动并行化。
• 在通道级别上自动并行化。

参数

模块

基础