摘要
非局部平均(NLM)是一种基于图像块之间相似性的加权平均去噪算法,对高斯噪声具有很好的抑制作用,但是在平滑区域的去噪效果并不是很好。从相似块的搜索区域和相似性度量函数两个方面对NLM算法进行了分析,指出其在平滑区域容易产生极值点的原因。提出了一种结合图像块特征的阈值方法,用于消除搜索区域中的无关图像块,提高了图像相似结构的利用率。实验表明,新算法对光滑区域和细微结构的去噪能力要优于NLM算法。
Non Local Means（NLM） is a state-of-the-art image blocks＇ similarity,It performs well on Gaussian method for image denoising based on a nonlocal weighted mean of noisy images.However, local extremal points are prone to yield in smoothing area.By analyzing strengths and weaknesses of NLM＇s searching area and similarity function,the reasons of extrereal points＇ appearance in smoothing area are given.A novel image denoising method based on NLM framework is presented to achieve improved performance.The new method adopts a compact weight set by using a threshold based on image block variation to eliminate irrelevant similar blocks.Compared with the original NLM,the new method is more efficient in keeping smooth in homogeneous areas and boundary maintenance according to the experimental results.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第29期192-195,共4页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.40806011)
江苏省自然科学基金(No.BK20082140)
连云港市软科学研究计划(No.RK1018)
淮海工学院自然科学基金(No.Z2009013)
关键词
去噪
非局部平均
相似性
度量函数
搜索区域
图像块
denoising
non-local means
similar
distance function
searching area
image block
