Abstract:
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.
Key words:
images de-noising,
wavelet transform,
adaptive slide windows,
Bayes threshold value,
neighboring coefficients,
relative coefficients
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。
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
CLC Number:
GONG Xiao-Lin, MAO Rui-Quan, LIU Kai-Hua. Wavelet Images Threshold Value De-noising Based on Adaptive Neighboring Coefficients[J]. Computer Engineering, 2010, 36(11): 206-208.
宫霄霖, 毛瑞全, 刘开华. 基于自适应邻域系数的小波图像阈值降噪[J]. 计算机工程, 2010, 36(11): 206-208.