结合随机场的自适应加权FCM改进方法  

Improved method of adaptive weight FCM combining random field

作　　者：林亚忠[1] 郝刚[2] 顾金库[2] 蔡茜[2] 

机构地区：[1]福建漳州第175医院(厦门大学附属东南医院),福建漳州363000 [2]厦门大学计算机科学系,福建厦门361005

出　　处：《计算机工程与应用》2011年第15期171-174,共4页Computer Engineering and Applications

基　　金：福建省自然科学基金No.2008J0312;南京军区"十一五"计划课题项目;南京军区重点课题~~

摘　　要：传统模糊C均值的隶属度场利用了像素的单点灰度信息,有利于算法保留细节,但去噪能力较弱;而图像的Gibbs随机场较好地刻画了像素的空间分布,有利于算法去噪,但在保留细节方面较差。该文利用邻域信息,动态地判断像素可能所在的位置,对两种场的权重进行自适应调整,从而实现两种场的优势互补。实验表明,该文自适应加权算法在去除噪声的同时可以保留更多的细节。Membership field of traditional fuzzy C means algorithm consider gray information of single pixel only,which is beneficial to retain details but weak in denoising an image.On the other hands, Gibbs random field depicts the spatial distribution of pixels, which is beneficial to smooth noise but poor at retaining image details.Thus, an improved method is proposed to take advantages over the two algorithms respectively,which can automatically determine the possible location of a pixel and adjust the proportion of the two fields according to neighborhood information of a pixel.Experiments show that the improved algorithm can adjust the weight of two fields adaptively to remove the noise while preserving more details.

关 键 词：隶属度场 GIBBS随机场 邻域标准差 自适应加权 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]