coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiffcoherence_enhancing_diff (算子)

名称

coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiffcoherence_enhancing_diff — 对图像执行相干增强扩散。

签名

描述

算子 coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiffCoherenceEnhancingDiffcoherence_enhancing_diff performs an anisotropic diffusion process on the input image ImageImageImageImageimageimage to increase the coherence of the image structures contained in ImageImageImageImageimageimage. In particular, noncontinuous image edges are connected by diffusion, without being smoothed perpendicular to their dominating direction. For this, coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiffCoherenceEnhancingDiffcoherence_enhancing_diff uses the anisotropic diffusion equation formulated by Weickert. With a 2x2 coefficient matrix G that depends on the gray values in ImageImageImageImageimageimage, this is an enhancement of the mean curvature flow or intrinsic heat equation on the gray value function u defined by the input image ImageImageImageImageimageimage at a time . The smoothing operator mean_curvature_flowmean_curvature_flowMeanCurvatureFlowMeanCurvatureFlowMeanCurvatureFlowmean_curvature_flow is a direct application of the mean curvature flow equation. The discrete diffusion equation is solved in IterationsIterationsIterationsIterationsiterationsiterations time steps of length ThetaThetaThetaThetathetatheta, so that the output image ImageCEDImageCEDImageCEDImageCEDimageCEDimage_ced contains the gray value function at the time 。

To detect the edge direction more robustly, in particular on noisy input data, an additional isotropic smoothing step can precede the computation of the gray value gradients. The parameter SigmaSigmaSigmaSigmasigmasigma determines the magnitude of the smoothing by means of the standard deviation of a corresponding Gaussian convolution kernel, as used in the operator isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion for isotropic image smoothing.

While the matrix G is given by in the case of the operator mean_curvature_flowmean_curvature_flowMeanCurvatureFlowMeanCurvatureFlowMeanCurvatureFlowmean_curvature_flow, where I denotes the unit matrix, is again smoothed componentwise by a Gaussian filter of standard deviation RhoRhoRhoRhorhorho for coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiffCoherenceEnhancingDiffcoherence_enhancing_diff. Then, the final coefficient matrix is constructed from the eigenvalues and eigenvectors of the resulting intermediate matrix, where the functions were determined empirically and taken from the publication of Weickert.

Hence, the diffusion direction in mean_curvature_flowmean_curvature_flowMeanCurvatureFlowMeanCurvatureFlowMeanCurvatureFlowmean_curvature_flow is only determined by the local direction of the gray value gradient, while considers the macroscopic structure of the image objects on the scale RhoRhoRhoRhorhorho and the magnitude of the diffusion in coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiffCoherenceEnhancingDiffcoherence_enhancing_diff depends on how well this structure is defined.

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

参考文献

J. Weickert, V. Hlavac, R. Sara; “Multiscale texture enhancement”; Computer analysis of images and patterns, Lecture Notes in Computer Science, Vol. 970, pp. 230-237; Springer, Berlin; 1995.
J. Weickert, B. ter Haar Romeny, L. Florack, J. Koenderink, M. Viergever; “A review of nonlinear diffusion filtering”; Scale-Space Theory in Computer Vision, Lecture Notes in Comp. Science, Vol. 1252, pp. 3-28; Springer, Berlin; 1997.

模块

基础