基于斯皮尔曼等级相关性的彩色图像椒盐噪点检测算法

Salt&pepper noise detection algorithm for color images based on Spearman rank correlation

为了避免在图像去噪时对非噪声点产生运算,更好地保护图像边缘细节,针对彩色图像设计了一种椒盐噪点检测方法.首先,将图像中的每个像素点作为待检测点,对待检测点及其左侧相邻像素点利用彩色图像的空间和色彩相关性构造两个6维向量;然后,算出两向量的斯皮尔曼等级相关系数,通过设置阈值T1筛选图像中的极值点,初选出所有可能的噪点和边缘点;最后,计算初选点与其周围8个像素点之间的斯皮尔曼等级相关系数,通过设置阈值T2将初选点确定为噪点或边缘点.仿真实验表明,利用粒子群优化算法对两个阈值参数寻优后的参考取值范围为0.28<T2<0.41<0.70<T1<0.80;用寻优的阈值,对椒盐噪声浓度介于0.01~0.1和0.2~0.8的含噪图像的噪点检测率分别不低于91.51%和88.01%,对加入人为噪点的含噪图像的噪点检测率不低于88.71%.该检测精度能够保证去噪后的彩色图像的CPSNR值超过57dB.

To avoid the operation on non-noise pixels,and preserve the original feature information of the image,which can protect the edge details and obtain high quality color images,a noise detection method based on Spearman rank correlation coefficient is designed for color images. Firstly, each pixel of the color image is taken as the point to be detected,and two six-dimensional vectors are constructed using the spatial and color correlation of the color image between the detection point and the adjacent pixel on the left side. Secondly, the Spearman rank correlation coefficient of the two vectors is calculated,and the extreme points of the image are selected by setting the threshold T 1,which is used to make comparisons with the Spearman rank correlation coefficients. Then, the comparative results are used to determine all the possible noise and edge points,called the extreme points. The primary point is determined as the extreme point if the Spearman rank correlation coefficient is smaller than the threshold T 1. Finally, eight Spearman correlation coefficients between the extreme point and its surrounding eight pixels are calculated,and the threshold value T 2 is set,then,comparisons are made between the eight Spearman rank correlation coefficients and T 2 to determine whether the extreme point is noise point or edge point. The simulation results show that the reference values of the two parameters optimized by particle swarm optimization algorithm are 0.28< T 2 <0.41<0.70< T 1<0.80. By using the threshold value of optimization,the detection rate of pepper and salt noise-containing images with a noise concentration between 0.01~0.1 and 0.2~0.8 is not less than 91.51% and 88.01%,respectively. Under the same conditions,if artificial noise is added to the standard color images,the noise detection accuracy of the noisy image is not lower than 88.71%. The detection accuracy can ensure that the CPSNR value of the denoised color image is more than 57dB.