基于小波阈值的非局部均值去噪

非局部均值去噪算法充分利用了图像的全局信息，因此比传统的局部去噪算法有着更好的去噪效果。但是，非局部均值去噪算法计算时间复杂度较高，故利用小波阂值的方法对其进行改进，改进后使用非局部均值处理的数据量大幅减小。实验表明，改进后的算法比非局部均值算法去噪效果基本持平，且运行速度更快。...

Full description

Saved in:
Bibliographic Details
Published in计算机工程与科学 Vol. 37; no. 8; pp. 1546 - 1550
Main Author 李嘉浪 李华君 徐庆
Format Journal Article
LanguageChinese
Published 天津大学计算机学院,天津,300072 2015
Subjects
图像去噪
小波阈值滤波
非局部均值
非局部均值
wavelet threshold filter
non-local means
图像去噪
image denoising
小波阈值滤波
Online AccessGet full text
ISSN1007-130X
DOI10.3969/j.issn.1007-130X.2015.08.019

Cover

More Information
Summary:非局部均值去噪算法充分利用了图像的全局信息，因此比传统的局部去噪算法有着更好的去噪效果。但是，非局部均值去噪算法计算时间复杂度较高，故利用小波阂值的方法对其进行改进，改进后使用非局部均值处理的数据量大幅减小。实验表明，改进后的算法比非局部均值算法去噪效果基本持平，且运行速度更快。
Bibliography:The non-local means denoising algorithm can use the globe information of the picture, therefore it has better denoising effect than other traditional algorithms. However, since its time complexity is high, we put forth a new non-local means denoising algorithm based on wavelet threshold filter, which use much less data than the traditional non-local means. Experimental results show that com- pared to the traditional nonqocal means, the denoising effect of our algorithm is basically the same, but the running speed is faster.
43-1258/TP
LI Jia-lang,LI Hua-jun,XU Qing （School of Computer Scienee,Tianjin University,Tianjin 300072 ,China）
non-local means ; wavelet threshold filter ; image denoising
ISSN:1007-130X
DOI:10.3969/j.issn.1007-130X.2015.08.019