小波变换三阈值多尺度融合边缘检测算法

充分利用边缘点和噪声点在梯度方向特征上的差异,提出了边缘点的梯度方向特征的概念。在不同尺度上对图像进行小波变换,得到每个像素点的梯度信息,利用双阈值的非极大值抑制法和边缘点的梯度方向特征提取每一尺度上的边缘点,最后用第三个阈值融合各尺度下的检测结果,得到图像边缘。实验结果证明,该算法与经典的Canny算子和Mallat小波算子相比,在保证边缘定位能力的同时,具有更强的抗噪声性,在强噪声干扰下仍可获得满意的边缘检测效果。

Improved wavelet-based 3 thresholds and multi-scale integration edge detection algorithm.

This paper proposes the 3 thresholds edge detection algorithm based on multi-scale integration in the wavelet domain, the advantage of this method is the excellent anti-noise performance. It gives the concept of gradient direction characteristics utilizing the differences between the edge point and noise point along the gradient direction. First of all, the wavelet transform for the image on the different scale hasbeendone to get gradient information of each pixel. Then it detects the edge point through the non-maximum inhibition method of dual-threshold and the gradient direction characteristics. At the last, the edge extracting results from the integration of the third threshold and the detection results in each scale are obtained. Compared with the classical Canny and Mallat operators, experiments denominate this algorithm not only has the good capacity of edge location, but also can extract the satisfactory results for the image involved with strong noise.