基于鲁棒主成分分析的Canny边缘检测算法

为提高图像边缘检测的准确性和鲁棒性,提出一种基于鲁棒主成分分析(RPCA)的Canny边缘检测算法。该算法对图像进行RPCA分解得到图像的主成分和稀疏成分,利用Canny算子对主成分进行边缘检测,从而实现对图像的边缘检测。该算法将图像的边缘检测问题转化为图像主成分的边缘检测问题,消除了图像信息中“污点”对检测结果的干扰,抑制了噪声。仿真实验结果表明,该算法在边缘检测的准确性和鲁棒性方面优于Log边缘检测算法、Canny边缘检测算法和Susan边缘检测算法方法。

Canny edge detection algorithm based on robust principal component analysis

To improve the accuracy and robustness of image edge detection, a new Canny edge detection algorithm based on Robust Principal Component Analysis (RPCA) was proposed. The image was decomposed into a principal component and a sparse component by RPCA. Then edge information of the principal component was extracted by Canny operator. The proposed algorithm formulated the problem of image edge detection as the edge detection of the principal component of the image. It eliminated the interference of image "stain" on the detection results and suppressed the noise. The experimental results show that the proposed algorithm outperforms Log, Canny and Susan edge detection algorithms in terms of both accuracy and robustness.