摘要
拉普拉斯(Laplace)边缘检测算子对噪声非常敏感。为了解决这个问题,在有限脊波变换(finite ridgelettransform,FRIT)域提出了一种边缘检测算法。首先用拉普拉斯分布模型分析FRIT系数,给出了FRIT系数的最大后验概率(maximum a posteriori,MAP)估计,同时结合边缘检测模型,得到了用于边缘检测的最优阈值,实现了图像的边缘检测。该算法抑制了噪声对图像边缘的影响,同时保护了图像的边缘。实验结果表明,在抑制噪声和边缘定位之间该方法能够达到更好的平衡,得到良好的边缘检测效果。
A Laplace operator is apt to be affected by noise.An edge detection algorithm finite ridgelet transform（FRIT）-based was proposed in order to improve it.Based on a Laplace probability distribution function（PDF）,the finite ridgelet-based transform coefficients were analyzed,and the maximum a posteriori（MAP） estimation for FRIT coefficients was developed.According to the model for some edge detectors,an optimal threshold for edge detection was obtained,and edge image was extracted.Both avoiding the effect of noise and preserving edge image were achieved.Experimental results showed that the proposed edge detector achieved better performances both on localization and avoiding the effect of noise,and edge image could be better distinguished by the proposed method.
出处
《山东大学学报（工学版）》
CAS
北大核心
2011年第4期113-118,共6页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(60872119)
山东省高等学校科技计划项目(J11LG85)
关键词
有限脊波变换
边缘检测
拉普拉斯算子
最大后验概率估计
finite ridgelet transform
edge detection
Laplace operator
maximum a posteriori estimation
