Image Denoising Based on Non-Local Means and Multi-scale Dyadic Wavelet Transform

被引：0
|
作者：
Yu, Gang [1 ]
Yin, Yong [1 ,2 ]
Wang, Hongjun [1 ]
Liu, Zhi [3 ]
Li, Dengwang [1 ]
机构：
[1] Shandong Univ, Sch Informat Sci & Engn, Jinan, Peoples R China
[2] Shandong Tumor Hosp, Dept Radiat Oncol, Jinan, Peoples R China
[3] Minist Educ, Key Lab Intelligent Comp & Informat Proc, Xiangtan, Peoples R China
来源：
PROCEEDINGS OF 2010 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (ICCSIT 2010), VOL 6 | 2010年
关键词：
denoising; non-local means; wavelet transform; multiscale singularity detection;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A variety of wavelet transform methods have been introduced to remove noise from images. However, many of these algorithms remove the fine details and smooth the structures of the image when removing noise. The wavelet coefficient magnitude sum (WCMS) algorithm can preserve edges, but it is at the expense of removing noise. The Non-Local means algorithm can removing noise effective. But it tend to cause distortion (eg white). Meanwhile, when the noise is large, the method is not so effective. In this paper, we propose an efficient denoising algorithm. we denoised the image with non-local means algorithm in the spatial domain and WCMS algorithm in wavelet domain, weithted, combined them and got the image that we want. The experiment shows that our algorithm can improve PSNR form 0.6dB to 1.0dB and the image boundary is more clearly.
引用
收藏
页码：333 / 336
页数：4
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