目的 椒盐噪声是造成图像污染的常见因素之一，椒盐噪声密度的估计对椒盐去噪过程中滤波窗口大小的选择具有指导作用。为此提出了一种基于分块策略的椒盐噪声密度估计算法。方法 首先对图像按行列等分后形成多个图像子块，统计每个子块中灰度为0或255的像素点个数并排序，然后根据排序后个数差分值函数特征对子块进行筛选，最后将所有候选子块噪声密度估计值的中值作为对整幅图像噪声密度的估计。结果 为验证算法的有效性，选取了两组不同类型的图像进行仿真，与现有椒盐噪声密度估计算法对比噪声密度估计结果。仿真实验结果表明，当图像自身包含较多灰度为0或255的像素点时，本文算法的噪声密度估计精度优于现有各种算法，标准差比现有算法小近一个数量级。当图像自身不包含灰度为0或255的像素点时，本文算法也能达到现有算法中最优的估计效果。结论 本文算法不仅能准确估计不同强度下的噪声密度，而且适用于自身包含灰度为0或255的像素点多的椒盐噪声图像。

Objective Salt-and-pepper noise is one of the most common factors causing image contamination. Salt-and-pepper noise estimation has a guiding role in determining the size of the filtering window in denoising. Thus, we propose an algorithm based on the partitioning strategy to estimate salt-and-pepper noise density.Method The proposed algorithm horizontally and vertically splits the image equally into sub-blocks, counts the pixels of the sub-blocks with gray levels of 0 or 255, sorts all sub-blocks, selects candidate sub-blocks according to the characteristics of the different equences of the sorted pixel numbers, and uses the median of the noise density estimations of all the candidate sub-blocks as to estimate the noise density of the whole image.Result To evaluate the proposed approach, two different types of images are processed using the presented Method, and the noise density estimation Results are compared with those of existing salt-and-pepper noise density estimation algorithms.Conclusion Simulation Results show that the new algorithm can accurately estimate noise density under different intensities and is effective for images that have many extreme pixels with gray levels of 0 or 255.