摘要
针对图像中椒盐噪声的抑制,提出了一种新的滤波算法。算法首先借助K-均值聚类将当前像素所在邻域的灰度分布进行有效划分;然后,构建噪声污染像素识别规则,借助多层二叉树决策实现不同类型噪声污染像素的检测。算法只针对噪声污染像素进行自适应滤波,而不改变非污染像素的取值。实验表明,本文算法在有效抑制噪声的同时可较好保留图像的细节等有用信息;对于噪声污染严重的图像,本算法明显优于传统中值滤波及文献[7]的算法。
In this paper, a new filter algorithm was proposed for pepper-and-salt noise suppression. Firstly, the neighborhood of each given pixel is partitioned by K-means clustering according to the local grey level distribution. Secondly, the recognition rules for noise-polluted pixel detection are constructed, and the noise pixel can be detected based on multi-layer binary tree decision. The proposed algorithm only filters the recognized noise pixels without changing those non-polluted pixel values. Experimental results show that the proposed algorithm can efficiently preserve informative details when filte- ring image noise. For those images with strong noise pollution, the proposed algorithm outperforms both median filter and the algorithm proposed in[7].
出处
《计算机工程与科学》
CSCD
北大核心
2013年第5期118-123,共6页
Computer Engineering & Science
基金
国家自然科学基金天元数学基金资助项目(10926179)
河北省科学技术重大支撑计划资助项目(10243554D)
河北省科学技术研究与发展计划资助项目(072435158D
09213515D
09213575D)
关键词
椒盐噪声
K-均值聚类
二叉树
图像滤波
噪声检测
pepper-and-salt noise
K-means clustering
binary tree
image filtering
noise detection
