摘要： 边界判别噪声检测(BDND)算法对不平衡椒盐噪声和随机值噪声检测效果不佳。针对该问题,提出一种基于改进BDND 的脉冲噪声滤波方法。修改BDND 分群不等式,将边界值b2 置于高灰度群中,利用BDND 第一阶段检测图像的所有像素点,生成噪声的直方图向量,通过比较相邻噪声数值的比值与给定阈值的关系,重新定义上下边界值,对中心像素做进一步检测。实验结果表明,该方法的检测性能明显优于BDND,漏检率和误检率大幅 降低,并且能够在消除噪声的同时更好地保护图像的细节信息。

关键词: 边界判别噪声检测, 脉冲噪声, 开关中值滤波, 阈值, 边界值, 图像去噪

Abstract: Aiming at the problem that Boundary Discriminative Noise Detection ( BDND ) performs poorly when detecting unbalanced salt-and-pepper noise or random-valued impulse noise. Based on estimated noise distribution,this paper proposes a modification of BDND. It modifies the clustering inequality by placing b2 in high intensity group. And it uses the first stage of BDND detecting all pixels to generate a noise histogram. And it redefines the upper and lower boundary values by comparing the ratio of adjacent noise with a given threshold value. It uses the new boundary values for further noise detection. Experimental results show that the proposed method is superior than BDND on detection performance,miss detection and false detection have been greatly reduced. On the visual effects,it can filter out noise while preserving image details well.

Key words: Boundary Discriminative Noise Detection(BDND), impulse noise, switching median filtering, threshold, boundary value, image denoising

中图分类号: 

• TP391