A novel statistical approach to remove salt-and-pepper noise.

Recently, image simulation has widely attracted people’s attentions. In this paper, we propose a novel statistical approach to remove salt-and-pepper noise. A statistic model of the number of noise pixels is built and the noise ratio of the corrupted image is estimated. To remove the noise, two steps including pixels analysis and noise removal are studied. Firstly, a statistical approach is proposed to analyse pixels to identify whether they are noise or not. Secondly, we adopt two different mean filters to remove noise with respect to corrupted images whose noise ratios are no more than 30% and above 30%, respectively. For a noiseless pixel, we keep its value unchanged. For a noisy pixel, we replace it with the mean value according to its corresponding noise ratio. Simulation results show that compared with some state-of-the-art methods, our method can effectively eliminate noise, hold more details and acquire larger values with two image quality metrics: peak signal to noise ratio and structural similarity. [ABSTRACT FROM AUTHOR]