Salt and pepper noise filtering algorithm based on local border gray-scale differences
沿边局部灰度差分椒盐噪声滤波

In order to reduce detection error in high density salt and pepper noise and to improve the filtering performance, a new local adaptive gray similarity bilateral filter algorithm is proposed in this paper. First, the algorithm extracts quasi-noise through gray-scale extremism of salt and pepper gray values, and then detects the local window border for every quasi-noise point to get the possible noise collection. Finally, only the pixels in the quasi-noise collection need to be filtered with nearby bilateral weighted gray-scale filtering. Experimental results demonstrate that the new algorithm has increased the parameters for peak signal-to-noise ratio(PSNR) by 0.2~1.6 dB in different noise density. Experimental results demonstrate that the new algorithm has the most integrated optimal performance in comparison to other filtering algorithms.