基于自适应邻域系数的小波图像阈值降噪  

Wavelet Images Threshold Value De-noising Based on Adaptive Neighboring Coefficients

作　　者：宫霄霖[1] 毛瑞全[1] 刘开华[1] 

机构地区：[1]天津大学电子信息工程学院,天津300072

出　　处：《计算机工程》2010年第11期206-208,共3页Computer Engineering

摘　　要：利用小波系数的层内相关性原理,并结合广义高斯模型,提出一种自适应邻域的阈值去噪方法。该方法通过计算以待处理系数为中心的不同邻域内的相关度系数,选择相关程度最好的邻域。对该方法中选择的邻域尺寸进行统计,发现分解的层次越高,较大的邻域出现的概率越大,这有利于保护边缘信息。实验结果表明,该方法优于固定邻域及阈值改进的邻域阈值方法,是一种有效的去噪方法。Using intra-scale dependency of wavelet coefficients and generalized Gaussian model, this paper proposes an adaptive neighboring threshold value de-noising method. By calculating the relative coefficients in the different neighborhood, a well neighborhood of relative coefficient is choosen. Through gathering statistics of neighboring size choosen, decomposition level is higher, the probability is biger in a large neighborhood, thus makes for profecting information. Experimental result shows that the method is better than that of fixed neighborhood and improved threshold value, it is a valid de-noiSing method.

关 键 词：图像去噪 小波变换 自适应滑窗 贝叶斯阈值 邻域系数 相关系数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]