相似邻居数目图像脉冲噪声滤波算法

Removal of impulse noise based on peer neighbor group

为了去除图像随机脉冲噪声的同时保留边缘,提出一种新方法。该方法首先利用图像局部灰度相似性来构造相似邻居数目图,一个像素的相似邻居数目在窗口内最大或数值较大,才可能认为是没有受到噪声干扰的像素。根据噪声密度不同采用不同方法检测。实验结果表明,阈值能适应性不同图像类型,滤波结果优于大部分已有算法,且算法复杂度低于大部分改进的中值滤波算法。

In this paper, a novel approach is proposed for removing and restoring random-valued impulse noise and preserving fine details at the same time. The impulse noise detection technique is presented,which is based on the so-called peer group concept. A pixel is noise-free only when its peer neighbor number is local maximum or large. Extensive simulations show that the proposed filter provides better performance than many of the existing filters and the computational complexity is lower than many of the median-based filters. In particular,the threshold is adaptive to different image types.