Bilateral filtering is effective for suppressing Gaussian noise but cannot filter out impulse noise. So we propose a de-noising algorithm mentioned in the title, which we believe is effective for suppressing both Gaussian noise and impulse noise. Sections 1 through 3 explain our de-noising algorithm and their core consists of; the bilateral filter has a good effect for suppressing the Gaussian noise but not for impulse noise; the recognized impulsive noise points are suppressed by the filtering based on the maximum of probability density and the remaining Gaussian noise points are still suppressed by bilateral filter; since the bilateral filter is nonlinear and only the recognized impulse noise points are filtered out differently, our algorithm can retain most of the edge features. The simulation results , presented in Fig. 1, and their analysis demonstrate the superiority of our method in suppressing the mixed noises, which consist of Gaussian noise and impulse noise, compared with the other de-noising methods of color image.%针对彩色图像中的混合噪声提出一种CIE-Lab颜色空间的混合去噪算法.双边滤波对高斯噪声具有不错的抑制效果,然而其固有不足是不能处理脉冲噪声,文章采取逆向思维方法将这种不足用于彩色图像脉冲噪声的识别,并仅对识别出的脉冲噪声点在CIE-Lab空间采用概率密度极值滤波方法进行滤除,对剩余高斯噪声仍利用双边滤波算法处理.文中算法采用双边滤波这种非线性滤波算法处理高斯噪声同时仅对识别出的脉冲噪声点进行概率密度极值滤波,因此该算法具有保留图像边缘特征的特性.最后仿真实验表明,CIE-Lab空间的混合滤波算法能够有效滤除高斯噪声和脉冲噪声,相比其他彩色图像噪声处理方法,该方法更为优越.
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