针对典型的两个边界随机值噪声检测问题,先进边界区分噪声检测(ABDND)通过全局的灰度值统计方法来确定噪声边界,取得了良好的检测效果。但是在噪声范围较宽时,ABDND的检测结果中会有大量的错检噪声。在ABDND的基础上提出一种噪声检测改进算法(MABDND),算法分为两个阶段:第1阶段采用ABDND算法中的全局灰度值统计方法;第2阶段通过对局部灰度值的统计找出第1阶段中的错检像素,并将错检噪声恢复为非噪声像素。本文算法的优点在于利用第2阶段的验证技巧去校正第1阶段中产生的大量错检像素,以保证较低的漏检与错检率。以图像Lena、peppers为实验对象,实验结果表明MABDND的检测性能优于ABDND,特别是在噪声范围较宽时,MABDND具有更好的检测性能和更强的噪声适应能力。

In this paper,we are aiming at random-valued impulse noise detection in two boundaries, using advanced boundary discriminative noise detection(ABDND)and a global histogram to obtain the noise boundary. Although we get good detection results, the rate of false detection increases for ABDND when the range of the noise boundary increases. A modification of the ABDND(MABDND)is therefore proposed in this paper. It includes two stages. First, it uses the global histogram to obtain the noise boundary identical to the ABDND. Second, it uses the statistic of a part of the histogram to find false detected pixels in the first stage, and marks them as uncorrupt pixels. The merit of MABDND is to use the confirmation technique in the second stage to rectify many false detectied pixels in the first stage to keep a low rate for both miss detection and false detection. Image Lena and peppers are used for simulations, and the experimental results show the performance of MABDND is better than that of ABDND, especially, when the range of random-valued is wide MABDND is more robust.