• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

基于截断奇异值低秩矩阵恢复的Canny边缘检测算法

郭伟,董宏亮,赵德冀   

  1. (辽宁工程技术大学软件学院,辽宁 葫芦岛 125105)
  • 收稿日期:2017-06-07 修回日期:2017-08-15 出版日期:2018-09-25 发布日期:2018-09-25
  • 基金资助:

    国家自然科学基金(61540056);辽宁省自然科学基金(2015020095)

A Canny edge detection algorithm based on
truncated singular value lowrank matrix recovery
 

GUO Wei,DONG Hongliang,ZHAO Deji   

  1. (School of Software,Liaoning Technical University,Huludao 125105,China)
  • Received:2017-06-07 Revised:2017-08-15 Online:2018-09-25 Published:2018-09-25

摘要:

针对Canny算法在处理噪声图像时存在的不足,为提高其准确性和鲁棒性,提出一种基于截断奇异值的低秩矩阵恢复方法,以及一种更加准确的双噪声凸优化模型和求解方法。使用经典Canny边缘检测算法作用于分解后去除冗余信息的主成分上,将图像的边缘检测转化为对主成分的边缘检测,可以在有效地去除脉冲噪声和高斯噪声干扰的同时,更好地保留边缘信息。为验证其有效性,在不同噪声浓度以及混合噪声情况下进行实验,结果分析表明,基于低秩矩阵恢复的边缘检测算法可以更好地保留完整的边缘信息,提高边缘检测的准确性及鲁棒性。

关键词: 边缘检测, 鲁棒主成分分析, 双噪声凸优化模型, 截断奇异值, 奇异值分解

Abstract:

In order to improve the accuracy and robustness of the Canny algorithm in processing noise images, we propose a low rank matrix recovery method based on truncated singular value, and present a more accurate dual noiseconvex optimization model and a method of solving the optimization model. We use the classical Canny edge detection method on the decomposed principal component without redundant information, and thus transform the edge detection of the image into the edge detection of the principal component. This method can effectively eliminate the impulse noise and Gaussian noise while preserving the edge information better. To verify its effectiveness, we conduct experiments under different noise concentrations and mixed noises, and the results show that the edge detection algorithm based on lowrank matrix recovery can better preserve the complete edge information and improve the accuracy and robustness of edge detection methods.
 

Key words: edge detection, robust principal component analysis, dual noise-convex optimization model, truncated singular value, singular value decomposition