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. 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. 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. 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.