为了在减小高密度椒盐噪声漏检的同时最大限度提升滤波效果,提出一种新算法。该算法首先利用椒盐灰度极值,抽取准噪声点;然后对准噪声点执行局部窗口沿边检测,判断疑似噪声集合;最后,只对疑似噪声集合中像素点进行就近双边加权灰度替换滤波。实验数据显示,不同噪声密度环境下,本文算法在现有滤波算法的基础上,将峰值信噪比(PSNR)参数分别提高0.2~1.6 dB。实验结果表明,该算法的综合性能优于其他相关的算法。

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.