基于Neyman-Pearson准则和小波模极大值的边缘检测  

Edge Detection Based on Neyman-Pearson Principle and Wavelet Modulus Maxima

作　　者：赵卫峰[1] 黄沄[1] 

机构地区：[1]重庆邮电大学资产管理处,重庆400065

出　　处：《价值工程》2015年第11期149-150,共2页Value Engineering

摘　　要：图像边缘是对图像进行进一步处理和识别的基础,边缘都是图像上灰度不连续的点或灰度变化剧烈的地方,所以说图像的边缘就是信号的高频部分,边缘检测就是检测信号的高频部分,是图像基本特征之一。但在实际的图像中,由于噪声的存在,边缘检测成为一个难题,所以本文提出了一种基于Neyman-Pearson准则和小波模极大值的边缘检测方法,该方法能很好地检测出图像的边缘。Image edge is one of the basic features, and the basic step in processing and identifying the image. Image edge is discontinuous point or change fierce point. This means that the edge of image is the high frequency part of signal. Edge detection is to detect the high frequency part of signal. But in practical image, edge detection becomes a problem because of the noises. So this paper points out a method based on the Neyman-Pearson principle and wavelet modulus maxima. The method can detect the edge of image very well.

关 键 词：Neyman—Pearson准则 小波模极大值 边缘检测 去噪 

分 类 号：TN911.7[电子电信—通信与信息系统]