采用先验知识的边缘提取算法

Edge Extraction Algorithm Based on Prior Knowledge

针对传统的边缘提取算法,在提取边缘时,不完整、不连续,尤其在高噪声情况下,无法提取图像边缘等问题,提出一种基于先验知识的边缘提取算法.首先,学习与待边缘处理图像有相似纹理信息的图像,获得先验知识,对噪声图纹理进行修复;然后,再利用局部均匀稀疏度方法强化细节特征,弱化背景特征;最后,检测出图像边缘,达到提取图像边缘的目的.实验结果表明,该算法能够克服传统边缘算子在边缘提取时,边缘不完整、不连续等缺点;同时,对强高斯噪声污染图像具有优秀的边缘提取效果.

In order to solve the problem that the traditional edge extraction algorithm is incomplete and discontinuous, especially in the case of high noise, an edge extraction algorithm based on prior knowledge is proposed. Firstly, the image with similar texture information to that to be processed is learned, and the prior knowledge is obtainedwith the texture of the noise image repaired. Then the local uniform sparsity method is used to strengthen the detail features and weaken the background features. Finally, the edge of the image is detected to achieve the purpose of extracting the edge of the image. The experimental results show that the algorithm can overcome the shortcomings of the traditional edge operator, such as incomplete edge extraction, discontinuity and so on. Meanwhile, it has excellent edge extraction effect on strong Gaussian noise pollution image.