基于曲率估计的Canny边缘检测算法

为了解决传统Canny边缘检测算法对噪声敏感的问题，针对噪声边缘弯曲度大且弯曲空间范围小的二维空间特性，构造了基于曲线曲率估计的边缘曲度算子，并利用大尺度Canny算法边缘检测结果对边缘曲度算子进行修正，使该算子能够准确地表征噪声强度在二维空间中的分布情况，在此基础上，提出了一种在边缘检测过程中加入边缘曲度算子进行噪声衰减的边缘检测算法. 实验结果表明，算法在有效抑制噪声的同时，保留了变化丰富的细节边缘及变化缓慢的轮廓边缘，检测结果较好地反映了图像的原始结构特征.

Canny Edge Detection Algorithm Based on Curvature Estimation

In order to solve the noise sensitive problem in the traditional Canny edge detection algorithm, based on the curvature estimation of curve, the edge curvature degree operator is constructed according to the noise edge characteristics which are large curvature and small curved space. The edge curvature degree operator is modified with the results of edge detection of the large scale Canny algorithm so that the operator can accurately characterize the distribution of the noise intensity in the two-dimensional space. On this basis, an edge detection algorithm based on edge curvature degree operator is proposed and the edge curvature degree operator can restrain the noise. The experimental results show that the algorithm cannot only suppress the noise effectively, but can also preserve the rich details edges and the slowly changing contour edges, and the detection results show the original structural features of the image.